mirror of
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1747 lines
140 KiB
Plaintext
1747 lines
140 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text",
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"id": "ya9j9pyzkyBZ"
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},
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"source": [
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"Copyright 2020 The \"What Can I do Here? A Theory of Affordances In Reinforcement Learning\" Authors. All rights reserved.\n",
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"\n",
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"Licensed under the Apache License, Version 2.0 (the \"License\");\n",
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"you may not use this file except in compliance with the License.\n",
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"You may obtain a copy of the License at\n",
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"\n",
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" https://www.apache.org/licenses/LICENSE-2.0\n",
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"\n",
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"Unless required by applicable law or agreed to in writing, software\n",
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"distributed under the License is distributed on an \"AS IS\" BASIS,\n",
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"WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
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"See the License for the specific language governing permissions and\n",
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"limitations under the License."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 0,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 34
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},
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"colab_type": "code",
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"id": "LbWb35G9UHLO",
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"outputId": "280cac1e-76e0-4960-a271-24d351f249bc"
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Populating the interactive namespace from numpy and matplotlib\n"
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]
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}
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],
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"source": [
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"%tensorflow_version 2.x\n",
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"%pylab inline\n",
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"\n",
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"# System imports\n",
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"import copy\n",
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"import dataclasses\n",
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"import enum\n",
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"import itertools\n",
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"import numpy as np\n",
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"import operator\n",
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"import random\n",
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"import time\n",
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"from typing import Optional, List, Tuple, Any, Dict, Union, Callable\n",
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"\n",
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"\n",
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"# Library imports.\n",
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"from google.colab import files\n",
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"from matplotlib import colors\n",
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"import matplotlib.animation as animation\n",
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"import matplotlib.pylab as plt\n",
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"import tensorflow as tf\n",
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"\n",
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"import tensorflow_probability as tfp"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text",
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"id": "UZV6OS_BUklD"
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},
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"source": [
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"# Environment Specification"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 0,
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"metadata": {
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"cellView": "form",
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"colab": {},
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"colab_type": "code",
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"id": "Mz4KtBOVUpOV"
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},
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"outputs": [],
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"source": [
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"#@title Point Class\n",
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"@dataclasses.dataclass(order=True, frozen=True)\n",
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"class Point:\n",
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" \"\"\"A class representing a point in 2D space.\n",
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"\n",
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" Comes with some convenience functions.\n",
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" \"\"\"\n",
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" x: float\n",
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" y: float\n",
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"\n",
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" def sum(self):\n",
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" return self.x + self.y\n",
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"\n",
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" def l2norm(self):\n",
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" \"\"\"Computes the L2 norm of the point.\"\"\"\n",
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" return np.sqrt(self.x * self.x + self.y * self.y)\n",
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"\n",
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" def __add__(self, other: 'Point'):\n",
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" return Point(self.x + other.x, self.y + other.y)\n",
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"\n",
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" def __sub__(self, other: 'Point'):\n",
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" return Point(self.x - other.x, self.y - other.y)\n",
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"\n",
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" def normal_sample_around(self, scale: float):\n",
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" \"\"\"Samples a point around the current point based on some noise.\"\"\"\n",
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" new_coords = np.random.normal(dataclasses.astuple(self), scale)\n",
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" new_coords = new_coords.astype(np.float32)\n",
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" return Point(*new_coords)\n",
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"\n",
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" def is_close_to(self, other: 'Point', diff: float = 1e-4):\n",
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" \"\"\"Determines if one point is close to another.\"\"\"\n",
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" point_diff = self - other\n",
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" if abs(point_diff.x) \u003c diff and abs(point_diff.y) \u003c diff:\n",
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" return True\n",
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" else:\n",
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" return False\n",
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"\n",
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"# Test the points.\n",
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"z1 = Point(0.4, 0.1)\n",
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"assert z1.is_close_to(z1)\n",
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"assert z1.is_close_to(Point(0.5, 0.0), 1.0)\n",
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"assert not z1.is_close_to(Point(5.0, 0.0), 1.0)\n",
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"z2 = Point(0.1, 0.1)\n",
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"z3 = z1 - z2\n",
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"assert isinstance(z3, Point)\n",
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"assert z3.is_close_to(Point(0.3, 0.0))\n",
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"assert isinstance(z3.normal_sample_around(0.1), Point)\n",
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"\n",
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"class Force(Point):\n",
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" pass\n",
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"\n",
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"\n",
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"# # Intersection code.\n",
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"# See Sedgewick, Robert, and Kevin Wayne. Algorithms. , 2011.\n",
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"# Chapter 6.1 on Geometric Primitives\n",
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"# https://algs4.cs.princeton.edu/91primitives/\n",
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"def _check_counter_clockwise(a: Point, b: Point, c: Point):\n",
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" \"\"\"Checks if 3 points are counter clockwise to each other.\"\"\"\n",
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" slope_AB_numerator = (b.y - a.y)\n",
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" slope_AB_denominator = (b.x - a.x)\n",
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" slope_AC_numerator = (c.y - a.y)\n",
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" slope_AC_denominator = (c.x - a.x)\n",
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" return (slope_AC_numerator * slope_AB_denominator \u003e= \\\n",
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" slope_AB_numerator * slope_AC_denominator)\n",
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"\n",
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"def intersect(segment_1: Tuple[Point, Point], segment_2: Tuple[Point, Point]):\n",
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" \"\"\"Checks if two line segments intersect.\"\"\"\n",
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" a, b = segment_1\n",
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" c, d = segment_2\n",
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"\n",
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" # Checking if there is an intersection is equivalent to:\n",
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" # Exactly one counter clockwise path to D (from A or B) via C.\n",
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" AC_ccw_CD = _check_counter_clockwise(a, c, d)\n",
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" BC_ccw_CD = _check_counter_clockwise(b, c, d)\n",
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" toD_via_C = AC_ccw_CD != BC_ccw_CD\n",
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"\n",
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" # AND\n",
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" # Exactly one counterclockwise path from A (to C or D) via B.\n",
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" AB_ccw_BC = _check_counter_clockwise(a, b, c)\n",
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" AB_ccw_BD = _check_counter_clockwise(a, b, d)\n",
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"\n",
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" fromA_via_B = AB_ccw_BC != AB_ccw_BD\n",
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"\n",
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" return toD_via_C and fromA_via_B\n",
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"\n",
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"# Some simple tests to ensure everything is working.\n",
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"assert not intersect((Point(1, 0), Point(1, 1)), (Point(0,0), Point(0, 1))), \\\n",
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" 'Parallel lines detected as intersecting.'\n",
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"assert not intersect((Point(0, 0), Point(1, 0)), (Point(0,1), Point(1, 1))), \\\n",
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" 'Parallel lines detected as intersecting.'\n",
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"assert intersect((Point(3, 5), Point(1, 1)), (Point(2, 2), Point(0, 1))), \\\n",
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" 'Lines that intersect not detected.'\n",
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"assert not intersect((Point(0, 0), Point(2, 2)), (Point(3, 3), Point(5, 1))), \\\n",
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" 'Lines that do not intersect detected as intersecting'\n",
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"assert intersect((Point(0, .5), Point(0, -.5)), (Point(.5, 0), Point(-.5, 0.))), \\\n",
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" 'Lines that intersect not detected.'"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 0,
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"metadata": {
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"cellView": "form",
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"colab": {},
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"colab_type": "code",
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"id": "IaC8khoBVZ2a"
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},
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"outputs": [],
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"source": [
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"#@title ContinuousWorld environment.\n",
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"\n",
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"class ContinuousWorld(object):\n",
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" r\"\"\"The ContinuousWorld Environment.\n",
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"\n",
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" An agent can be anywhere in the grid. The agent provides Forces to move. When\n",
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" the agent provides a force, it is applied and the final position is jittered.\n",
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"\n",
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" When the agent is reset, its location is drawn from a global start position\n",
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" given by `drift_between`. This start position is non-stationary and drifts\n",
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" toward the target start position as the environment resets with the speed\n",
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" `drift_speed`.\n",
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"\n",
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" For example the start position is (0., 0.). After reseting once, the start\n",
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" positon might drift toward (0.5, 0.5). After resetting again it may drift\n",
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" again to (0., 0.). This happens smoothly according to the drifting speed.\n",
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"\n",
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" Walls can be specified in this environment. Detection works by checking if the\n",
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" agents action forces it to go in a direction which collides with a wall.\n",
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" \"\"\"\n",
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"\n",
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" def __init__(\n",
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" self,\n",
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" size: float,\n",
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" wall_pairs: Optional[List[Tuple[Point, Point]]] = None,\n",
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" drift_between: Optional[List[Tuple[Point, Point]]] = None,\n",
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" movement_noise: float = 0.1,\n",
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" seed: int = 1,\n",
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" drift_speed: float = 0.5,\n",
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" reset_noise: Optional[float] = None,\n",
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" max_episode_length: int = 10,\n",
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" max_action_force: float = 0.5,\n",
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" verbose_reset: bool = False\n",
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" ):\n",
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" \"\"\"Initializes the Continuous World Environment.\n",
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"\n",
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" Args:\n",
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" size: The size of the world.\n",
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" wall_pairs: A list of tuple of points representing the start and end\n",
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" positions of the wall.\n",
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" drift_between: A list of tuple of points representing how the starting\n",
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" distrubiton should change. If None, it will drift between the four\n",
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" corners of the room.\n",
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" movement_noise: The noise around each position after movement.\n",
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" seed: The seed for the random number generator.\n",
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" drift_speed: How quickly to move in the drift direction.\n",
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" reset_noise: The noise around the reset position. Defaults to\n",
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" movement_noise if not specified.\n",
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" max_episode_length: The maximum length of the episode before resetting.\n",
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" max_action_force: If using random_step() this will be the maximum random\n",
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" force applied in the x and y direction.\n",
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" verbose_reset: Prints out every time the global starting position is\n",
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" reset.\n",
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" \"\"\"\n",
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" self._size = size\n",
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" self._wall_pairs = wall_pairs or []\n",
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" self._verbose_reset = verbose_reset\n",
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"\n",
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" # Points to drift the start position between.\n",
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" if drift_between is None:\n",
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" self._drift_between = (\n",
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" Point((1/4) * size, (1/4) * size),\n",
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" Point((1/4) * size, (3/4) * size),\n",
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" Point((3/4) * size, (1/4) * size),\n",
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" Point((3/4) * size, (3/4) * size),\n",
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" )\n",
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" else:\n",
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" self._drift_between = drift_between\n",
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"\n",
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" self._noise = movement_noise\n",
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" self._reset_noise = reset_noise or movement_noise\n",
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" self._rng = np.random.RandomState(seed)\n",
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" random.seed(seed)\n",
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"\n",
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" # The current and target starting positions.\n",
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" # Internal to this class mu is used to refer to mean \"start position\".\n",
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" # Therefore mu = current start position and end_mu is the target start\n",
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" # position.\n",
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" self._mu, self._end_mu = random.sample(self._drift_between, 2)\n",
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" # The speed at which we will move toward the target position.\n",
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" self._drift_speed = drift_speed\n",
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" self.update_agent_position()\n",
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" self._decide_new_target_mu()\n",
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" self._max_episode_length = max_episode_length\n",
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" self._current_episode_length = 0\n",
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" self._terminated = True\n",
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" self._max_action_force = max_action_force\n",
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" self._recent_mu_updated = False\n",
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"\n",
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" def _decide_new_target_mu(self):\n",
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" \"\"\"Decide a new target direction to move toward.\"\"\"\n",
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" # The direction should be toward the \"target ending mu.\"\n",
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" (new_end_mu,) = random.sample(self._drift_between, 1)\n",
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" while new_end_mu == self._end_mu:\n",
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" (new_end_mu,) = random.sample(self._drift_between, 1)\n",
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"\n",
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" self._end_mu = new_end_mu\n",
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" self._decide_drift_direction()\n",
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" if self._verbose_reset:\n",
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" print(f'Target mu has been updated to: {self._end_mu}')\n",
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" self._recent_mu_updated = True\n",
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"\n",
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" def _decide_drift_direction(self):\n",
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" \"\"\"Decide the drifting direction to move in.\"\"\"\n",
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" direction = self._end_mu - self._mu\n",
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" l2 = direction.l2norm()\n",
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" drift_direction = Point(direction.x / l2, direction.y / l2)\n",
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" self._drift_direction = Point(\n",
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" drift_direction.x * self._drift_speed,\n",
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" drift_direction.y * self._drift_speed\n",
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" )\n",
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"\n",
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" def _should_update_target_mu(self) -\u003e bool:\n",
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" \"\"\"Decide if the drift direction should change.\"\"\"\n",
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" # Condition 1: We are past the edge of the environment.\n",
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" if self._past_edge(self._mu.x)[0] or self._past_edge(self._mu.y)[0]:\n",
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" return True\n",
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"\n",
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" # Condition 2: Check if the current mu is close to the end mu.\n",
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" return self._mu.is_close_to(self._end_mu, self._drift_speed)\n",
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"\n",
|
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" def update_current_start_position(self):\n",
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" \"\"\"Update the current mu to drift toward mu_end. Change mu_end if needed.\"\"\"\n",
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" if self._should_update_target_mu():\n",
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" self._decide_new_target_mu()\n",
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" self._decide_drift_direction()\n",
|
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" proposed_mu = self._mu + self._drift_direction\n",
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" self._mu = self._wrap_coordinate(proposed_mu)\n",
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"\n",
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" def _past_edge(self, x: float) -\u003e Tuple[bool, float]:\n",
|
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" \"\"\"Checks if coordinate is beyond the edges.\"\"\"\n",
|
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" if x \u003e= self._size:\n",
|
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" return True, self._size\n",
|
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" elif x \u003c= 0.0:\n",
|
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" return True, 0.0\n",
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" else:\n",
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" return False, x\n",
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"\n",
|
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" def _wrap_coordinate(self, point: Point) -\u003e Point:\n",
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" \"\"\"Wraps coordinates that are beyond edges.\"\"\"\n",
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" wrapped_coordinates = map(self._past_edge, dataclasses.astuple(point))\n",
|
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" return Point(*map(operator.itemgetter(1), wrapped_coordinates))\n",
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"\n",
|
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" def update_agent_position(self):\n",
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" self._current_position = self._wrap_coordinate(\n",
|
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" self._mu.normal_sample_around(self._noise))\n",
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"\n",
|
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" def set_agent_position(self, new_position: Point):\n",
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" self._current_position = self._wrap_coordinate(new_position)\n",
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"\n",
|
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" def reset(self) -\u003e Tuple[float, float]:\n",
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" \"\"\"Reset the current position of the agent and move the global mu.\"\"\"\n",
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" self.update_current_start_position()\n",
|
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" self.update_agent_position()\n",
|
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" self._current_episode_length = 0\n",
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" self._terminated = False\n",
|
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" return self._current_position\n",
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"\n",
|
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" def get_random_force(self) -\u003e Force:\n",
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" return Force(*self._rng.uniform(\n",
|
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" -self._max_action_force, self._max_action_force, 2))\n",
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"\n",
|
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" def random_step(self):\n",
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" random_action = self.get_random_force()\n",
|
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" to_be_returned = self.step(random_action)\n",
|
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" to_be_returned[-1]['action_taken'] = random_action\n",
|
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" return to_be_returned\n",
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"\n",
|
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" @property\n",
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" def agent_position(self):\n",
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" return dataclasses.astuple(self._current_position)\n",
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"\n",
|
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" @property\n",
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" def start_position(self):\n",
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" return dataclasses.astuple(self._mu)\n",
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"\n",
|
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" @property\n",
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" def size(self):\n",
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" return self._size\n",
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"\n",
|
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" @property\n",
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" def walls(self):\n",
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" return self._wall_pairs\n",
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"\n",
|
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" def _check_goes_through_wall(self, start: Point, end: Point):\n",
|
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" if not self._wall_pairs: return False\n",
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"\n",
|
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" for pair in self._wall_pairs:\n",
|
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" if intersect((start, end), pair):\n",
|
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" return True\n",
|
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" return False\n",
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"\n",
|
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" def step(\n",
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" self,\n",
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" action: Force\n",
|
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" ) -\u003e Tuple[Tuple[float, float], Optional[float], bool, Dict[str, Any]]:\n",
|
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" \"\"\"Does a step in the environment using the action.\n",
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"\n",
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" Args:\n",
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" action: Force applied by the agent.\n",
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"\n",
|
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" Returns:\n",
|
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" Agent position: A tuple of two floats.\n",
|
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" The reward.\n",
|
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" An indicator if the episode terminated.\n",
|
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" A dictionary containing any information about the step.\n",
|
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" \"\"\"\n",
|
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" if self._terminated:\n",
|
|
" raise ValueError('Episode is over. Please reset the environment.')\n",
|
|
" perturbed_action = action.normal_sample_around(self._noise)\n",
|
|
"\n",
|
|
" proposed_position = self._wrap_coordinate(\n",
|
|
" self._current_position + perturbed_action)\n",
|
|
"\n",
|
|
" goes_through_wall = self._check_goes_through_wall(\n",
|
|
" self._current_position, proposed_position)\n",
|
|
"\n",
|
|
" if not goes_through_wall:\n",
|
|
" self._current_position = proposed_position\n",
|
|
"\n",
|
|
" self._current_episode_length += 1\n",
|
|
"\n",
|
|
" if self._current_episode_length \u003e self._max_episode_length:\n",
|
|
" self._terminated = True\n",
|
|
"\n",
|
|
" recent_mu_updated = self._recent_mu_updated\n",
|
|
" self._recent_mu_updated = False\n",
|
|
" return (\n",
|
|
" self._current_position,\n",
|
|
" None,\n",
|
|
" self._terminated,\n",
|
|
" {\n",
|
|
" 'goes_through_wall': goes_through_wall,\n",
|
|
" 'proposed_position': proposed_position,\n",
|
|
" 'recent_start_position_updated': recent_mu_updated\n",
|
|
" }\n",
|
|
" )"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 0,
|
|
"metadata": {
|
|
"cellView": "form",
|
|
"colab": {},
|
|
"colab_type": "code",
|
|
"id": "O43gq5h_YS2I"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"#@title Visualization suite.\n",
|
|
"\n",
|
|
"def visualize_environment(\n",
|
|
" world,\n",
|
|
" ax,\n",
|
|
" scaling=1.0,\n",
|
|
" agent_color='r',\n",
|
|
" agent_size=0.2,\n",
|
|
" start_color='g',\n",
|
|
" draw_agent=True,\n",
|
|
" draw_start_mu=True,\n",
|
|
" draw_target_mu=True,\n",
|
|
" draw_walls=True,\n",
|
|
" write_text=True):\n",
|
|
" \"\"\"Visualize the continuous grid world.\n",
|
|
"\n",
|
|
" The agent will be drawn as a circle. The start and target\n",
|
|
" locations will be drawn by a cross. Walls will be drawn in\n",
|
|
" black.\n",
|
|
"\n",
|
|
" Args:\n",
|
|
" world: The continuous gridworld to visualize.\n",
|
|
" ax: The matplotlib axes to draw the gridworld.\n",
|
|
" scaling: Scale the plot by this factor.\n",
|
|
" agent_color: Color of the agent.\n",
|
|
" agent_size: Size of the agent in the world.\n",
|
|
" start_color: Color of the start marker.\n",
|
|
" draw_agent: Boolean that controls drawing agent.\n",
|
|
" draw_start_mu: Boolean that controls drawing starting position.\n",
|
|
" draw_target_mu: Boolean that controls drawing ending position.\n",
|
|
" draw_walls: Boolean that controls drawing walls.\n",
|
|
" write_text: Boolean to write text for each component being drawn.\n",
|
|
" \"\"\"\n",
|
|
" scaled_size = scaling * world.size\n",
|
|
"\n",
|
|
" # Draw the outer walls.\n",
|
|
" ax.hlines(0, 0, scaled_size)\n",
|
|
" ax.hlines(scaled_size, 0, scaled_size)\n",
|
|
" ax.vlines(scaled_size, 0, scaled_size)\n",
|
|
" ax.vlines(0, 0, scaled_size)\n",
|
|
"\n",
|
|
" for wall_pair in world.walls:\n",
|
|
" ax.plot(\n",
|
|
" [p.x * scaling for p in wall_pair],\n",
|
|
" [p.y * scaling for p in wall_pair],\n",
|
|
" color='k')\n",
|
|
"\n",
|
|
" if draw_start_mu:\n",
|
|
" # Draw the position of the start dist.\n",
|
|
" x, y = [p * scaling for p in world.mu_start_position]\n",
|
|
" ax.scatter([x], [y], marker='x', c=start_color)\n",
|
|
" if write_text: ax.text(x, y, 'Starting position.')\n",
|
|
"\n",
|
|
" if draw_target_mu:\n",
|
|
" # Draw the target position.\n",
|
|
" x, y = [p * scaling for p in dataclasses.astuple(world._end_mu)]\n",
|
|
" ax.scatter([x], [y], marker='x', c='k')\n",
|
|
" if write_text: ax.text(x, y,'Target position.')\n",
|
|
"\n",
|
|
" if draw_agent:\n",
|
|
" # Draw the position of the agent as a circle.\n",
|
|
" x, y = [scaling * p for p in world.agent_position]\n",
|
|
" agent_circle = plt.Circle((x, y), agent_size, color=agent_color)\n",
|
|
" ax.add_artist(agent_circle)\n",
|
|
" if write_text: ax.text(x, y, 'Agent position.')\n",
|
|
"\n",
|
|
" return ax\n",
|
|
"\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"colab_type": "text",
|
|
"id": "eSfUwRtoY_aN"
|
|
},
|
|
"source": [
|
|
"# Affordance specification"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 0,
|
|
"metadata": {
|
|
"cellView": "form",
|
|
"colab": {},
|
|
"colab_type": "code",
|
|
"id": "IVTYi4YVZAfR"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"#@title Intent detection and plotting code.\n",
|
|
"\n",
|
|
"IntentName = enum.IntEnum(\n",
|
|
" 'IntentName', 'delta_pos_x delta_neg_x delta_pos_y delta_neg_y')\n",
|
|
"\n",
|
|
"class IntentStatus(enum.IntEnum):\n",
|
|
" complete = 1\n",
|
|
" incomplete = 0\n",
|
|
"\n",
|
|
"@dataclasses.dataclass(eq=False)\n",
|
|
"class Intent:\n",
|
|
" name: 'IntentName'\n",
|
|
" status: 'IntentStatus'\n",
|
|
"\n",
|
|
"\n",
|
|
"PointOrFloatTuple = Union[Point, Tuple[float, float]]\n",
|
|
"\n",
|
|
"def _get_intent_completed(\n",
|
|
" s_t: PointOrFloatTuple,\n",
|
|
" a_t: Force,\n",
|
|
" s_tp1: PointOrFloatTuple,\n",
|
|
" intent_name: IntentName,\n",
|
|
" threshold: float = 0.0):\n",
|
|
" r\"\"\"Determines if the intent was completed in the transition.\n",
|
|
"\n",
|
|
" The available intents are based on significant movement on the x-y plane:\n",
|
|
"\n",
|
|
" Intent is 1 if:\n",
|
|
" `s_tp1.{{x,y}} - s_t.{{x,y}} {{\u003e,\u003c}} threshold`\n",
|
|
" else: 0.\n",
|
|
"\n",
|
|
" Args:\n",
|
|
" s_t: The current position of the agent.\n",
|
|
" a_t: The force for the action.\n",
|
|
" s_tp1: The position after executing action of the agent.\n",
|
|
" intent_name: The intent that needs to be detected.\n",
|
|
" threshold: The significance threshold for the intent to be detected.\n",
|
|
" \"\"\"\n",
|
|
" if not isinstance(s_t, Point):\n",
|
|
" s_t = Point(*s_t)\n",
|
|
" if not isinstance(s_tp1, Point):\n",
|
|
" s_tp1 = Point(*s_tp1)\n",
|
|
" IntentName(intent_name) # Check if valid intent_name.\n",
|
|
"\n",
|
|
" diff = s_tp1 - s_t # Find the positional difference.\n",
|
|
"\n",
|
|
" if intent_name == IntentName.delta_pos_x:\n",
|
|
" if diff.x \u003e threshold:\n",
|
|
" return IntentStatus.complete\n",
|
|
" if intent_name == IntentName.delta_pos_y:\n",
|
|
" if diff.y \u003e threshold:\n",
|
|
" return IntentStatus.complete\n",
|
|
" if intent_name == IntentName.delta_neg_x:\n",
|
|
" if diff.x \u003c -threshold:\n",
|
|
" return IntentStatus.complete\n",
|
|
" if intent_name == IntentName.delta_neg_y:\n",
|
|
" if diff.y \u003c -threshold:\n",
|
|
" return IntentStatus.complete\n",
|
|
"\n",
|
|
" return IntentStatus.incomplete\n",
|
|
"\n",
|
|
"# Some simple test cases.\n",
|
|
"assert not _get_intent_completed(\n",
|
|
" Point(0, 0), None, Point(0.5, 0.0), IntentName.delta_neg_y)\n",
|
|
"assert not _get_intent_completed(\n",
|
|
" Point(0, 0), None, Point(0.5, 0.0), IntentName.delta_pos_y)\n",
|
|
"assert _get_intent_completed(\n",
|
|
" Point(0, 0), None, Point(0.5, 0.0), IntentName.delta_pos_x)\n",
|
|
"assert not _get_intent_completed(\n",
|
|
" Point(0, 0), None, Point(0.5, 0.0), IntentName.delta_neg_x)\n",
|
|
"assert _get_intent_completed(\n",
|
|
" Point(0, 0), None, Point(0.5, 0.5), IntentName.delta_pos_x)\n",
|
|
"assert _get_intent_completed(\n",
|
|
" Point(0, 0), None, Point(0.5, 0.5), IntentName.delta_pos_y)\n",
|
|
"assert not _get_intent_completed(\n",
|
|
" Point(0, 0), None, Point(0.5, 0.5), IntentName.delta_pos_y, 0.6)\n",
|
|
"assert not _get_intent_completed(\n",
|
|
" Point(0, 0), None, Point(-0.5, -0.5), IntentName.delta_neg_x, 0.6)\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 0,
|
|
"metadata": {
|
|
"cellView": "form",
|
|
"colab": {},
|
|
"colab_type": "code",
|
|
"id": "OtWqqse4Zutx"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"#@title Data Collection code\n",
|
|
"def get_transitions(\n",
|
|
" world: ContinuousWorld,\n",
|
|
" max_num_transitions: int = 500,\n",
|
|
" max_trajectory_length: Optional[int] = None,\n",
|
|
" policy: Optional[Callable[[np.ndarray], int]] = None,\n",
|
|
" intent_threshold: float = 0.0):\n",
|
|
" \"\"\"Samples transitions from an environment.\n",
|
|
"\n",
|
|
" Args:\n",
|
|
" world: The environment to collect trajectories from.\n",
|
|
" max_num_transitions: The total number of transitions to sample.\n",
|
|
" max_trajectory_length: The maximum length of the trajectory. If None\n",
|
|
" trajectories will naturally reset during episode end.\n",
|
|
" policy: The data collection policy. If None is given a random policy\n",
|
|
" is used. The policy must take a single argument, the one hot\n",
|
|
" representation of the state. If using a tensorflow function make sure to\n",
|
|
" handle batching within the policy itself.\n",
|
|
" intent_threshold: The threshold to use for the intent.\n",
|
|
"\n",
|
|
" Returns:\n",
|
|
" The transitions collected from the environment:\n",
|
|
" This is a 4-tuple containing the batch of state, action, state' and intent\n",
|
|
" target.\n",
|
|
" Human Readable transitions:\n",
|
|
" A set containing the unique transitions in the batch and if the intent was\n",
|
|
" completed.\n",
|
|
" Infos:\n",
|
|
" A list containing the info dicts sampled during the batch.\n",
|
|
" \"\"\"\n",
|
|
" max_trajectory_length = max_trajectory_length or float('inf')\n",
|
|
" trajectory = []\n",
|
|
" s_t = world.reset()\n",
|
|
" trajectory_length = 0\n",
|
|
" human_readable = set()\n",
|
|
" if policy is None:\n",
|
|
" def policy(_):\n",
|
|
" return world.get_random_force()\n",
|
|
"\n",
|
|
" infos = []\n",
|
|
"\n",
|
|
" for _ in range(max_num_transitions):\n",
|
|
" action = policy(s_t)\n",
|
|
" s_tp1, _, done, info = world.step(action)\n",
|
|
" infos.append(info)\n",
|
|
" reward = 0\n",
|
|
"\n",
|
|
" all_intents = []\n",
|
|
" intent_status_only = []\n",
|
|
" for intent_name in IntentName:\n",
|
|
" intent_status = _get_intent_completed(\n",
|
|
" s_t, action, s_tp1, intent_name, intent_threshold)\n",
|
|
" all_intents.append((intent_name, intent_status))\n",
|
|
" intent_status_only.append(intent_status)\n",
|
|
"\n",
|
|
" # Human readable vesion:\n",
|
|
" human_readable.add((s_t, action, s_tp1, tuple(all_intents)))\n",
|
|
"\n",
|
|
" # Prepare things for tensorflow:\n",
|
|
" s_t_tf = tf.constant(dataclasses.astuple(s_t), dtype=tf.float32)\n",
|
|
" s_tp1_tf = tf.constant(dataclasses.astuple(s_tp1), dtype=tf.float32)\n",
|
|
" a_t_tf = tf.constant(dataclasses.astuple(action), dtype=tf.float32)\n",
|
|
" intent_statuses_tf = tf.constant(intent_status_only)\n",
|
|
" trajectory.append((s_t_tf, a_t_tf, s_tp1_tf, reward, intent_statuses_tf))\n",
|
|
"\n",
|
|
" trajectory_length += 1\n",
|
|
" if done or trajectory_length \u003e max_trajectory_length:\n",
|
|
" s_t = world.reset()\n",
|
|
" trajectory_length = 0\n",
|
|
" else:\n",
|
|
" s_t = s_tp1\n",
|
|
"\n",
|
|
" batch = list(map(tf.stack, zip(*trajectory)))\n",
|
|
" return batch, human_readable, infos\n",
|
|
"\n",
|
|
"# Integration test.\n",
|
|
"world = ContinuousWorld(\n",
|
|
" size=2,\n",
|
|
" drift_speed=0.1,\n",
|
|
" max_action_force=2.0,\n",
|
|
" max_episode_length=100)\n",
|
|
"data, _, _ = get_transitions(world, max_num_transitions=2)\n",
|
|
"assert data is not None"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 0,
|
|
"metadata": {
|
|
"cellView": "form",
|
|
"colab": {},
|
|
"colab_type": "code",
|
|
"id": "bvYwGLyubcpa"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"#@title Probabilistic transition model\n",
|
|
"\n",
|
|
"hidden_nodes = 32\n",
|
|
"input_size = 2\n",
|
|
"\n",
|
|
"class TransitionModel(tf.keras.Model):\n",
|
|
" def __init__(self, hidden_nodes, output_size):\n",
|
|
" super().__init__()\n",
|
|
" self._net1 = tf.keras.layers.Dense(\n",
|
|
" hidden_nodes, activation=tf.keras.activations.relu)\n",
|
|
" self._net2 = tf.keras.layers.Dense(\n",
|
|
" hidden_nodes, activation=tf.keras.activations.relu)\n",
|
|
" # Multiply by 2 for means and variances.\n",
|
|
" self._output = tf.keras.layers.Dense(2*output_size)\n",
|
|
"\n",
|
|
" def __call__(self, st, at):\n",
|
|
" net_inputs = tf.concat((st, at), axis=1)\n",
|
|
" means_logstd = self._output(self._net2(self._net1(net_inputs)))\n",
|
|
" means, logstd = tf.split(means_logstd, 2, axis=1)\n",
|
|
" std = tf.exp(logstd)\n",
|
|
" return tfp.distributions.Normal(loc=means, scale=std)\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 0,
|
|
"metadata": {
|
|
"cellView": "form",
|
|
"colab": {},
|
|
"colab_type": "code",
|
|
"id": "5S6uqTVAdoKD"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"#@title Training algorithm.\n",
|
|
"\n",
|
|
"MACHINE_EPSILON = np.finfo(float).eps.item()\n",
|
|
"\n",
|
|
"def train_networks(\n",
|
|
" world: ContinuousWorld,\n",
|
|
" model_network: Optional[tf.keras.Model] = None,\n",
|
|
" model_optimizer: Optional[tf.keras.optimizers.Optimizer] = None,\n",
|
|
" affordance_network: Optional[tf.keras.Model] = None,\n",
|
|
" affordance_optimizer: Optional[tf.keras.optimizers.Optimizer] = None,\n",
|
|
" use_affordance_to_mask_model: bool = False,\n",
|
|
" affordance_mask_threshold: float = 0.9,\n",
|
|
" num_train_steps: int =10,\n",
|
|
" fresh_data: bool = True,\n",
|
|
" max_num_transitions: int = 1,\n",
|
|
" max_trajectory_length: Optional[int] = None,\n",
|
|
" optimize_performance: bool = False,\n",
|
|
" intent_threshold: float = 1.0,\n",
|
|
" debug: bool = False,\n",
|
|
" print_losses: bool = False,\n",
|
|
" print_every: int = 10):\n",
|
|
" \"\"\"Trains an affordance network.\n",
|
|
"\n",
|
|
" Args:\n",
|
|
" world: The gridworld to collect training data from.\n",
|
|
" model_network: The network for the transition model.\n",
|
|
" model_optimizer: The optimizer for the transition model.\n",
|
|
" affordance_network: The affordance network.\n",
|
|
" affordance_optimizer: The optimizer for the affordance network.\n",
|
|
" use_affordance_to_mask_model: Uses affordances to mask the losses of the\n",
|
|
" transition model.\n",
|
|
" affordance_mask_threshold: The threshold at which the mask should be\n",
|
|
" applied.\n",
|
|
" num_train_steps: The total number of training steps.\n",
|
|
" fresh_data: Collect fresh data before every training step.\n",
|
|
" max_num_transitions: The number of rollout trajectories per training step.\n",
|
|
" max_trajectory_length: The maximum length of each trajectory. If None then\n",
|
|
" there is no artifically truncated trajectory length.\n",
|
|
" optimizer_performance: Use `tf.function` to speed up training steps.\n",
|
|
" intent_threshold: The threshold to consider as a signficant completion of\n",
|
|
" the intent.\n",
|
|
" debug: Debug mode prints out the human readable transitions and disables\n",
|
|
" tf.function.\n",
|
|
" print_losses: Prints out the losses during training.\n",
|
|
" print_every: Indicates how often things should be printed out.\n",
|
|
" \"\"\"\n",
|
|
" all_aff_losses = []\n",
|
|
" all_model_losses = []\n",
|
|
"\n",
|
|
" # Error checking to make sure the correct combinations of model/affordance\n",
|
|
" # nets and optimizers are given or none at all.\n",
|
|
" if (affordance_network is None) != (affordance_optimizer is None):\n",
|
|
" raise ValueError('Both affordance network and optimizer have to be given.')\n",
|
|
" else:\n",
|
|
" use_affordances = affordance_network is not None\n",
|
|
"\n",
|
|
" if (model_network is None) != (model_optimizer is None):\n",
|
|
" raise ValueError('Both model network and optimizer have to be given.')\n",
|
|
" else:\n",
|
|
" use_model = model_network is not None\n",
|
|
"\n",
|
|
" # At least one of affordance network or model network must be specified.\n",
|
|
" if model_network is None and (\n",
|
|
" (model_network is None) == (affordance_network is None)):\n",
|
|
" raise ValueError(\n",
|
|
" 'This code does not do anything without models or affordances.')\n",
|
|
"\n",
|
|
" # Check if both are specified if use_affordance_to_mask_model is True.\n",
|
|
" if use_affordance_to_mask_model and (\n",
|
|
" model_network is None and affordance_network is None):\n",
|
|
" raise ValueError(\n",
|
|
" 'Cannot use_affordance_to_mask model if affordance and model networks'\n",
|
|
" ' are not given!')\n",
|
|
"\n",
|
|
" # User friendly print outs indicate what is happening.\n",
|
|
" print(\n",
|
|
" f'Using model? {use_model}. Using affordances? {use_affordances}. Using'\n",
|
|
" f' affordances to mask model? {use_affordance_to_mask_model}.')\n",
|
|
"\n",
|
|
" def _train_step_affordances(trajectory):\n",
|
|
" \"\"\"Train affordance network.\"\"\"\n",
|
|
" # Note: Please make sure you understand the shapes here before editing to\n",
|
|
" # prevent accidental broadcast.\n",
|
|
" with tf.GradientTape() as tape:\n",
|
|
" s_t, a_t, _, _, intent_target = trajectory\n",
|
|
" concat_input = tf.concat((s_t, a_t), axis=1)\n",
|
|
" preds = affordance_network(concat_input)\n",
|
|
"\n",
|
|
" intent_target = tf.reshape(intent_target, (-1, 1))\n",
|
|
" unshaped_preds = preds\n",
|
|
" preds = tf.reshape(preds, (-1, 1))\n",
|
|
"\n",
|
|
" loss = tf.keras.losses.binary_crossentropy(intent_target, preds)\n",
|
|
" total_loss = tf.reduce_mean(loss)\n",
|
|
" grads = tape.gradient(total_loss, affordance_network.trainable_variables)\n",
|
|
" affordance_optimizer.apply_gradients(\n",
|
|
" zip(grads, affordance_network.trainable_variables))\n",
|
|
"\n",
|
|
" return total_loss, unshaped_preds\n",
|
|
"\n",
|
|
" def _train_step_model(trajectory, affordances):\n",
|
|
" \"\"\"Train model network.\"\"\"\n",
|
|
" with tf.GradientTape() as tape:\n",
|
|
" s_t, a_t, s_tp1, _, _ = trajectory\n",
|
|
" transition_model = model_network(s_t, a_t)\n",
|
|
" log_prob = tf.reduce_sum(transition_model.log_prob(s_tp1), -1)\n",
|
|
" num_examples = s_t.shape[0]\n",
|
|
"\n",
|
|
" if use_affordance_to_mask_model:\n",
|
|
" # Check if at least one intent is affordable.\n",
|
|
" masks_per_intent = tf.math.greater_equal(\n",
|
|
" affordances, affordance_mask_threshold)\n",
|
|
" masks_per_transition = tf.reduce_any(masks_per_intent, 1)\n",
|
|
" # Explicit reshape to prevent accidental broadcasting.\n",
|
|
" batch_size = len(s_t)\n",
|
|
" log_prob = tf.reshape(log_prob, (batch_size, 1))\n",
|
|
" masks_per_transition = tf.reshape(masks_per_transition, (batch_size, 1))\n",
|
|
" log_prob = log_prob * tf.cast(masks_per_transition, dtype=tf.float32)\n",
|
|
" # num_examples changes if there is masking so take that into account:\n",
|
|
" num_examples = tf.reduce_sum(\n",
|
|
" tf.cast(masks_per_transition, dtype=tf.float32))\n",
|
|
" num_examples = tf.math.maximum(num_examples, tf.constant(1.0))\n",
|
|
"\n",
|
|
" # Negate log_prob here because we want to maximize this via minimization.\n",
|
|
" total_loss = -tf.reduce_sum(log_prob) / num_examples\n",
|
|
" grads = tape.gradient(total_loss, model_network.trainable_variables)\n",
|
|
" model_optimizer.apply_gradients(\n",
|
|
" zip(grads, model_network.trainable_variables))\n",
|
|
"\n",
|
|
" return total_loss\n",
|
|
"\n",
|
|
" # Optimize performance using tf.function.\n",
|
|
" if optimize_performance and not debug:\n",
|
|
" _train_step_affordances = tf.function(_train_step_affordances)\n",
|
|
" _train_step_model = tf.function(_train_step_model)\n",
|
|
" print('Training step has been optimized.')\n",
|
|
"\n",
|
|
" initial_data_collected = False\n",
|
|
" infos = []\n",
|
|
" for i in range(num_train_steps):\n",
|
|
" # Step 1: Collect data.\n",
|
|
" if not initial_data_collected or fresh_data:\n",
|
|
" initial_data_collected = True\n",
|
|
" running_time = time.time()\n",
|
|
" trajectories, unique_transitions, infos_i = get_transitions(\n",
|
|
" world,\n",
|
|
" max_num_transitions=max_num_transitions,\n",
|
|
" max_trajectory_length=max_trajectory_length,\n",
|
|
" intent_threshold=intent_threshold)\n",
|
|
" collection_running_time = time.time() - running_time\n",
|
|
" if debug: print('unique_transitions:', unique_transitions)\n",
|
|
" running_time = time.time()\n",
|
|
"\n",
|
|
" # Check if the start state was updated:\n",
|
|
" infos.append(\n",
|
|
" any([info['recent_start_position_updated'] for info in infos_i]))\n",
|
|
"\n",
|
|
" # Step 2: Train affordance model.\n",
|
|
" if use_affordances:\n",
|
|
" aff_loss, affordance_predictions = _train_step_affordances(trajectories)\n",
|
|
" aff_loss = aff_loss.numpy().item()\n",
|
|
" else:\n",
|
|
" affordance_predictions = tf.constant(0.0) # Basically a none.\n",
|
|
" aff_loss = None\n",
|
|
" all_aff_losses.append(aff_loss)\n",
|
|
"\n",
|
|
" # Step 3: Train transition model and mask predictions if necessary.\n",
|
|
" if use_model:\n",
|
|
" model_loss = _train_step_model(trajectories, affordance_predictions)\n",
|
|
" model_loss = model_loss.numpy().item()\n",
|
|
" else:\n",
|
|
" model_loss = None\n",
|
|
" all_model_losses.append(model_loss)\n",
|
|
"\n",
|
|
" if debug or print_losses:\n",
|
|
" if i % print_every == 0:\n",
|
|
" train_loop_time = time.time() - running_time\n",
|
|
" print(f'i: {i}, aff_loss: {aff_loss}, model_loss: {model_loss}, '\n",
|
|
" f'collection_loop_time: {collection_running_time:.2f}, '\n",
|
|
" f'train_loop_time: {train_loop_time:.2f}')\n",
|
|
"\n",
|
|
" return all_model_losses, all_aff_losses, infos"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"colab_type": "text",
|
|
"id": "6hXi7Iy0b50_"
|
|
},
|
|
"source": [
|
|
"# Plotting utilities"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 0,
|
|
"metadata": {
|
|
"cellView": "form",
|
|
"colab": {},
|
|
"colab_type": "code",
|
|
"id": "gMNIsCLEb3Ie"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"#@title Learning curve smoothing\n",
|
|
"# From https://github.com/google-research/policy-learning-landscape/blob/master/analysis_tools/data_processing.py#L82\n",
|
|
"\n",
|
|
"DEFAULT_SMOOTHING_WEIGHT = 0.9\n",
|
|
"def apply_linear_smoothing(data, smoothing_weight=DEFAULT_SMOOTHING_WEIGHT):\n",
|
|
" \"\"\"Smooth curves using a exponential linear weight.\n",
|
|
"\n",
|
|
" This smoothing algorithm is the same as the one used in tensorboard.\n",
|
|
"\n",
|
|
" Args:\n",
|
|
" data: The sequence or list containing the data to smooth.\n",
|
|
" smoothing_weight: A float representing the weight to place on the moving\n",
|
|
" average.\n",
|
|
"\n",
|
|
" Returns:\n",
|
|
" A list containing the smoothed data.\n",
|
|
" \"\"\"\n",
|
|
" if len(data) == 0: # pylint: disable=g-explicit-length-test\n",
|
|
" raise ValueError('No data to smooth.') \n",
|
|
" if smoothing_weight \u003c= 0:\n",
|
|
" return data\n",
|
|
" last = data[0]\n",
|
|
" smooth_data = []\n",
|
|
" for x in data:\n",
|
|
" if not np.isfinite(last):\n",
|
|
" smooth_data.append(x)\n",
|
|
" else:\n",
|
|
" smooth_data.append(last * smoothing_weight + (1 - smoothing_weight) * x)\n",
|
|
" last = smooth_data[-1]\n",
|
|
" return smooth_data\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 0,
|
|
"metadata": {
|
|
"cellView": "form",
|
|
"colab": {},
|
|
"colab_type": "code",
|
|
"id": "T5cvk1XUb-M0"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"#@title Intent plotting code.\n",
|
|
"\n",
|
|
"def plot_intents(\n",
|
|
" world: ContinuousWorld,\n",
|
|
" affordance_predictions: np.ndarray,\n",
|
|
" eval_action: Tuple[float, float],\n",
|
|
" num_world_ticks: int = 3,\n",
|
|
" intent_collection: IntentName = IntentName,\n",
|
|
" subplot_configuration: Tuple[int, int] = (2, 2),\n",
|
|
" figsize: Tuple[int, int] = (5, 5)):\n",
|
|
" \"\"\"Plots the intents as a heatmap.\n",
|
|
"\n",
|
|
" Given the predictions from the affordance network, we plot a heatmap for each\n",
|
|
" intent indicating how likely the `eval_action` can be used to complete it.\n",
|
|
"\n",
|
|
" Args:\n",
|
|
" world: The gridworld to use.\n",
|
|
" affordance_predictions: Predictions from the affordance classifier. The last\n",
|
|
" dimension should be of the same len as intent_collection.\n",
|
|
" eval_action: The eval action being used (For plotting the title).\n",
|
|
" num_world_ticks: The number of ticks on the axes of the world.\n",
|
|
" subplot_configuration: The arrangement of the subplots on the plot.\n",
|
|
" figsize: The size of the matplotlib figure.\n",
|
|
" \"\"\"\n",
|
|
" fig = plt.figure(figsize=figsize)\n",
|
|
"\n",
|
|
" # Since we are predicting probabilities, normalize between 0 and 1.\n",
|
|
" norm = mpl.colors.Normalize(vmin=0.0, vmax=1.0)\n",
|
|
"\n",
|
|
" # The colorbar axes.\n",
|
|
" cax = fig.add_axes([1.0, 0.1, 0.075, 0.8])\n",
|
|
"\n",
|
|
" for intent in intent_collection:\n",
|
|
" ax = fig.add_subplot(*subplot_configuration, intent)\n",
|
|
" afford_sliced = affordance_predictions[:, :, intent-1]\n",
|
|
" afford_sliced = np.transpose(afford_sliced)\n",
|
|
" ax_ = ax.imshow(afford_sliced, origin='lower')\n",
|
|
"\n",
|
|
" # This code will handle num_world_ticks=0 gracefully.\n",
|
|
" ax.set_xticks(np.linspace(0, afford_sliced.shape[0], num_world_ticks))\n",
|
|
" ax.set_yticks(np.linspace(0, afford_sliced.shape[0], num_world_ticks))\n",
|
|
" ax.set_xticklabels(\n",
|
|
" np.linspace(0, world.size, num_world_ticks), fontsize='x-small')\n",
|
|
" ax.set_yticklabels(\n",
|
|
" np.linspace(0, world.size, num_world_ticks), fontsize='x-small')\n",
|
|
"\n",
|
|
" ax.set_xlabel('x')\n",
|
|
" ax.set_ylabel('y', rotation=0)\n",
|
|
" plt.title('Intent: {}'.format(intent.__repr__()[-10:-2]))\n",
|
|
" ax_.set_norm(norm)\n",
|
|
" if intent == len(intent_collection):\n",
|
|
" plt.colorbar(ax_, cax)\n",
|
|
" cax.set_ylabel('Probability of intent completion')\n",
|
|
"\n",
|
|
" plt.suptitle('Evaluating Action: {}'.format(eval_action))\n",
|
|
" plt.tight_layout(rect=[0, 0.03, 1, 0.95])"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"colab_type": "text",
|
|
"id": "pHarhdiSdixz"
|
|
},
|
|
"source": [
|
|
"# Main Experiment (Training)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 0,
|
|
"metadata": {
|
|
"colab": {},
|
|
"colab_type": "code",
|
|
"id": "Zq9TXAyDfaP_"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"# Storing the losses and models in a global list.\n",
|
|
"all_losses_global = []\n",
|
|
"all_models_global = []\n",
|
|
"all_affordance_global = []"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 0,
|
|
"metadata": {
|
|
"cellView": "form",
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/",
|
|
"height": 1000
|
|
},
|
|
"colab_type": "code",
|
|
"id": "OP50CV_YdkLi",
|
|
"outputId": "58a4677b-5dcf-4015-d795-2e7a4b9384fc"
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Experiments that will be run: [False, True]\n",
|
|
"Resetting seed to 0.\n",
|
|
"Target mu has been updated to: Point(x=0.5, y=0.5)\n",
|
|
"Using model? True. Using affordances? False. Using affordances to mask model? False.\n",
|
|
"Training step has been optimized.\n",
|
|
"Target mu has been updated to: Point(x=1.5, y=1.5)\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:107: RuntimeWarning: invalid value encountered in double_scalars\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"i: 0, aff_loss: None, model_loss: 3.079850912094116, collection_loop_time: 0.20, train_loop_time: 0.28\n",
|
|
"i: 1000, aff_loss: None, model_loss: -1.3993345499038696, collection_loop_time: 0.17, train_loop_time: 0.00\n",
|
|
"Target mu has been updated to: Point(x=0.5, y=0.5)\n",
|
|
"i: 2000, aff_loss: None, model_loss: -1.4746335744857788, collection_loop_time: 0.17, train_loop_time: 0.00\n",
|
|
"Target mu has been updated to: Point(x=1.5, y=1.5)\n",
|
|
"i: 3000, aff_loss: None, model_loss: -1.926070213317871, collection_loop_time: 0.29, train_loop_time: 0.00\n",
|
|
"i: 4000, aff_loss: None, model_loss: -2.129803419113159, collection_loop_time: 0.17, train_loop_time: 0.00\n",
|
|
"Target mu has been updated to: Point(x=0.5, y=0.5)\n",
|
|
"i: 5000, aff_loss: None, model_loss: -2.212693452835083, collection_loop_time: 0.18, train_loop_time: 0.00\n",
|
|
"Target mu has been updated to: Point(x=1.5, y=1.5)\n",
|
|
"i: 6000, aff_loss: None, model_loss: -2.104724168777466, collection_loop_time: 0.18, train_loop_time: 0.00\n",
|
|
"i: 7000, aff_loss: None, model_loss: -2.153459310531616, collection_loop_time: 0.19, train_loop_time: 0.00\n",
|
|
"Target mu has been updated to: Point(x=0.5, y=0.5)\n",
|
|
"Resetting seed to 0.\n",
|
|
"Target mu has been updated to: Point(x=0.5, y=0.5)\n",
|
|
"Using model? True. Using affordances? True. Using affordances to mask model? True.\n",
|
|
"Training step has been optimized.\n",
|
|
"Target mu has been updated to: Point(x=1.5, y=1.5)\n",
|
|
"i: 0, aff_loss: 0.7048008441925049, model_loss: 3.4398090839385986, collection_loop_time: 0.17, train_loop_time: 0.66\n",
|
|
"i: 1000, aff_loss: 0.19042730331420898, model_loss: -1.7565302848815918, collection_loop_time: 0.19, train_loop_time: 0.00\n",
|
|
"Target mu has been updated to: Point(x=0.5, y=0.5)\n",
|
|
"i: 2000, aff_loss: 0.23882852494716644, model_loss: -1.7699742317199707, collection_loop_time: 0.18, train_loop_time: 0.00\n",
|
|
"Target mu has been updated to: Point(x=1.5, y=1.5)\n",
|
|
"i: 3000, aff_loss: 0.207383394241333, model_loss: -1.983075737953186, collection_loop_time: 0.17, train_loop_time: 0.00\n",
|
|
"i: 4000, aff_loss: 0.1946447789669037, model_loss: -2.051856517791748, collection_loop_time: 0.19, train_loop_time: 0.00\n",
|
|
"Target mu has been updated to: Point(x=0.5, y=0.5)\n",
|
|
"i: 5000, aff_loss: 0.21516098082065582, model_loss: -1.9648913145065308, collection_loop_time: 0.17, train_loop_time: 0.00\n",
|
|
"Target mu has been updated to: Point(x=1.5, y=1.5)\n",
|
|
"i: 6000, aff_loss: 0.20555077493190765, model_loss: -1.8827911615371704, collection_loop_time: 0.18, train_loop_time: 0.00\n",
|
|
"i: 7000, aff_loss: 0.17789226770401, model_loss: -2.0646157264709473, collection_loop_time: 0.28, train_loop_time: 0.00\n",
|
|
"Target mu has been updated to: Point(x=0.5, y=0.5)\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": 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8r4D3ga/l6w0GZgHXAjPz7W0FniF7yu8NmzbxSFMT06dPp7GxkZtvvrnVNjzzzDMsXbqUAQMGcPzxx3P22WcjiT/+8Y+MGTOGhx56iBNOOIEJEyawcuVKDjroIH6Z38GyYcOG7bZ14403cvPNN9PU1LRDPffffz9NTU0899xzvPHGGxx//PF8+tOfBuDZZ59l2bJlHHTQQZx00kk8+eSTjBs3bmd+pVaFfETYGz3+eM88aJXsdHhyPj2Zj06P5wNfIPsDOgA4OS9/EVhKdpRYB/wvsvFfm52X/zwOeCUie2hsB0477TT23Xdf+vfvz3nnncf8+fOZP38+++67LzU1NdTW1nLeeefxxBNPcMwxxzB37lyuuuoqnnjiCfbee++y93X+/PlMmTKFmpoa9t9/fyZMmMDChQsBGDt2LEOGDGGXXXahrq6OV155peztWvXzEWFv1ENfRl5PNkD1ErLOkm35z/a+ARjAUZSM4NXC7vnPGrKjQ37TcReMWgwK1fJ9qSOOOILFixczZ84crrvuOk499VSmTZvW5vLl2n333T+crqmpYWvzKH/WJ/iIsDfqoaPBe4EvAb8nOw1+FTgUeIJsOML7yK4V/hGYl6/zCWAtHwXh+8Cy9irZvJm99tqLt9t5RNfcuXNZv349mzdv5sEHH+Skk05i/PjxrFu3jm3btvHOO+/wwAMPMH78eF577TUGDBjAhRdeyJVXXsnixYt32F6/fv14v5Wndo8fP55Zs2axbds21q5dy+OPP87YsWPba731EQ7C3qiHHrV1J3Bui7Lz8/LzyQa4HglcCIwB9gZ2IwvQq4DRZKfH7fbJbtnCySefzPLly1vtLIHstPT8889n1KhRnH/++dTX1zNmzBj2339/Fi9ezAknnMCll17Ksccey5IlSxg7dix1dXXccMMNXHfddTtsb+rUqYwaNerDzpJm5557LqNGjWL06NGccsopfPe73+WAAw5o93c0bdo0Zs+e3e4yVv1Ujb1f9fX10djYWNayDQ0NQNazl4zDD4eXX650K9gI1ALryHqCnyS7Xtgp++7b7tOrb7/99jY7UpL87A3Y+c9e0qKIqG9ZXtYRoaSJkl6UtELS1a3Mv1jSWklN+evSknkXSXopf13UqVZb60quV1XSn5Md8Y0H/ic7EYJQNftiaeuws0RSDXALWUfgKmChpNmtDNQ+KyIub7HuPsD1QD3ZdfRF+bq+ybQrDjigR79Q3ZZ5RWxk0KB2Z1988cVc7Md2WTcr54hwLLAiIlZGxBay79VOKnP7ZwBzI2J9Hn5zyb6WZl0xYQLs2gc6/CUYP77SrTArKwgPJuswbLYqL2vpfEnPS7pX0tBOrmudMXYsDBhQ6VZ0XW0t+D5fqwJF9Rr/OzA8IkaRHfX9a2c3IGmqpEZJjWvXri2oWX1UfX3vehhrWz74IBv32KzCygnC1cDQkvdD8rIPRcS6iGj+TsdtZDcOlLVuyTZmRER9RNQPHjy4nLana9Ag6MQdE1Xrgw/g4x+vdCvMygrChcAISYdK2o3sTqvtvjgl6cCSt+cAL+TTDwOnSxooaSBwel5mXXXuub37OqEEZ5yRPZfQrMI6/CuMiK3A5WQB9gJwd0QskzRd0jn5Yn8raZmk54C/BS7O110PfIssTBcC0/My66qvfx369at0K3begAFw1VWVboUZUOa9xhExh+yBIaVl00qmrwGuaWPdmXz08BEryic+AcceC731WXpDhsAJJ1S6FWaAb7Hr3a65Bvbaq9Kt6LzaWvjmN7PTY7Mq4CDszc48s3d2mvTrB3/xF5VuhdmHHIS9WU1NNohT//6Vbkn5BgyAn/2sx56wbVYOB2Fvd9JJ8JWv9I4vWO+xB5x3Hpx9dqVbYrYdB2Ff8J3vZPcfV7u994Yf/rDSrTDbgYOwL9h9d7jvvuo+KuzfH+6+u3d27lif5yDsK+rq4J57qvN6Yf/+2WDu+UBIZtXGQdiXnHUW/Nu/VVcY9u+fDeT+xS9WuiVmbXIQ9jXnnQe/+EV1nCb3758F8yWXVLolZu1yEPZFp50GTz6ZPdCgEoHYvz8cdBA88kgWzGZVzkHYV9XVwQsvwN//fRZMPXUXR//+cNllsGKFnzVovYaDsC/r1w9uuAEaG+GYY7Jb27pLbW02qNQTT8A//mN1Xac064CDMAUjR8Kzz2ZfsTnttOzrNkXc2bHbbtl2Pv1puOOO7Aj0uOM6Xs+syvTiB9pZp+yyC5x+evZ67TX453+GH/0I/vSnLMw2bYJWBj3fzq67wp57ZuMqDxgAf/3X2WnwIYf0zD6YdRMHYYoOOgiuvz57rVmTHS0uWpSd1i5Z8lEofvDBR0ePI0dmR3719dnjvw48sON6zHoJB2Hq9tsve1L0GWdUuiVmFeNrhGaWPAehmSXPQWhmyXMQmlnyHIRmljwHoZklr6wglDRR0ouSVki6upX5X5e0XNLzkn4t6ZCSedskNeWv2S3XNTOrtA6/RyipBrgFOA1YBSyUNDsilpcs9ixQHxGbJF0GfBf4b/m8zRFRV3C7zcwKU84R4VhgRUSsjIgtwF3ApNIFIuKxiNiUv10ADCm2mWZm3aecIDwYeLXk/aq8rC2XAA+VvN9DUqOkBZI+txNtNDPrVoXeYifpQqAemFBSfEhErJZ0GPCopCUR8XIr604FpgIMGzasyGaZmbWrnCPC1cDQkvdD8rLtSPoMcC1wTkS811weEavznyuBecCxrVUSETMioj4i6gcPHlz2DpiZdVU5QbgQGCHpUEm7AZOB7Xp/JR0L/JgsBNeUlA+UtHs+PQg4CSjtZDEzq7gOT40jYquky4GHgRpgZkQskzQdaIyI2cD3gFrgHmWPhP9DRJwDfBL4saQPyEL3xha9zWZmFVfWNcKImAPMaVE2rWT6M22s9xRwTFcaaGbW3XxniZklz0FoZslzEJpZ8hyEZpY8B6GZJc9BaGbJcxCaWfIchGaWPAehmSXPQWhmyXMQmlnyHIRmljwHoZklz0FoZslzEJpZ8hyEZpY8B6GZJc9BaGbJcxCaWfIchGaWPAehmSXPQWhmySsrCCVNlPSipBWSrm5l/u6SZuXzn5Y0vGTeNXn5i5LOKK7pZmbF6DAIJdUAtwBnAiOBKZJGtljsEuDNiDgc+D7wv/N1RwKTgaOAicAP8+2ZmVWNcgZ4HwusiIiVAJLuAiYBy0uWmQT8Qz59L3CzJOXld0XEe8DvJK3It/efxTQ/09TURENDQ5GbtF6gqakJwJ99gpqamqirqytse+WcGh8MvFryflVe1uoyEbEV2ADsW+a6AEiaKqlRUuPatWvLaz0wb968Qn8h1nvU1tZSW1tb6WZYBdTV1TFv3rzCtlfOEWGPiIgZwAyA+vr66My6Rf5CzCw95RwRrgaGlrwfkpe1uoykXYG9gXVlrmtmVlHlBOFCYISkQyXtRtb5MbvFMrOBi/LpzwOPRkTk5ZPzXuVDgRHAM8U03cysGB2eGkfEVkmXAw8DNcDMiFgmaTrQGBGzgZ8AP8s7Q9aThSX5cneTdaxsBb4aEdu6aV/MzHaKsgO36lJfXx+NjY2VboaZ9TGSFkVEfcty31liZslzEJpZ8hyEZpY8B6GZJc9BaGbJcxCaWfIchGaWvKr8HqGktcDvO7HKIOCNbmqO66/eulOvP+V939n6D4mIwS0LqzIIO0tSY2tfknT9fbvu1OtPed+Lrt+nxmaWPAehmSWvrwThDNefZN2p15/yvhdaf5+4Rmhm1hV95YjQzGynVX0QVnIo0TLq/rqk5ZKel/RrSYeUzNsmqSl/tXyQbVH1XyxpbUk9l5bMu0jSS/nropbrFlT/90vq/q2kP5XM69L+S5opaY2kpW3Ml6Qf5G17XtKYknlF7HtH9V+Q17tE0lOSRpfMeyUvb5LU6efJlVF3g6QNJb/faSXz2v3MCqr/ypK6l+af9T75vC7te76NoZIey/9tLZN0RSvLFPv5R0TVvsgeBPsycBiwG/AcMLLFMn8D3JpPTwZm5dMj8+V3Bw7Nt1NTcN0nAwPy6cua687fb+yBfb8YuLmVdfcBVuY/B+bTA4uuv8XyXyN7aG9R+/9pYAywtI35ZwEPAQI+BTxd1L6XWf+JzdslG+r26ZJ5rwCDunHfG4D/29XPbGfrb7HsZ8meSF/IvufbOBAYk0/vBfy2lb/9Qj//aj8i/HAo0YjYAjQPJVpqEvCv+fS9wKnS9kOJRsTvgOahRAurOyIei4hN+dsFZGOyFKWcfW/LGcDciFgfEW8Cc8nGle7O+qcAd3ayjjZFxONkTztvyyTgp5FZAHxM0oEUs+8d1h8RT+Xbh4I/+zL2vS1d+ZvZ2foL/dzz+l+PiMX59NvAC+w4+mWhn3+1B2GPDCXahbpLXUL2P1SzPZQNT7pA0uc6UW9n6z8/PzW4V1LzQFld3fdObSO/JHAo8GhJcVf3f2fbV8S+d1bLzz6AX0laJGlqN9X5Z5Kek/SQpKPysh7dd0kDyELmvpLiQvdd2aWuY4GnW8wq9POvmuE8ezNJFwL1wISS4kMiYrWkw4BHJS2JiJcLrvrfgTsj4j1J/53syPiUgusox2Tg3th+PJqe2P+Kk3QyWRCOKykel+/7fsBcSb/Jj7KKspjs97tR0lnAg2QDo/W0zwJPRkTp0WNh+y6plixk/y4i3iqgvW2q9iPCSg4lWtb6kj4DXAucExHvNZdHxOr850pgHtn/ap3RYf0Rsa6kztuA4zrT9q7WX2IyLU6PCtj/nW1fjw0hK2kU2e99UkSsay4v2fc1wAN07pJMhyLirYjYmE/PAfpJGkTPD5/b3ufepX2X1I8sBO+IiPtbWaTYz78rFzW7+0V2xLqS7LSr+eLvUS2W+Srbd5bcnU8fxfadJSvpXGdJOXUfS3ZxekSL8oHA7vn0IOAlOnnRusz6DyyZPhdYEB9dMP5d3o6B+fQ+RdefL3ck2QVyFbn/+brDabvD4Gy2v1j+TFH7Xmb9w8iuO5/YonxPYK+S6aeAiQXXfUDz75ssaP6Q/x7K+sy6Wn8+f2+y64h7dsO+C/gpcFM7yxT6+Xf6F9TTL7Leod+SBc61edl0siMwgD2Ae/I/ymeAw0rWvTZf70XgzG6o+xHgj0BT/pqdl58ILMn/EJcAl3TTvn8HWJbX8xhwZMm6f5X/TlYAX+6O+vP3/wDc2GK9Lu8/2ZHG68D7ZNd5LgG+Anyl5B/LLXnblgD1Be97R/XfBrxZ8tk35uWH5fv9XP7ZXNsNdV9e8rkvoCSMW/vMiq4/X+Ziss7I0vW6vO/5dsaRXWt8vuT3e1Z3fv6+s8TMklft1wjNzLqdg9DMkucgNLPkOQjNLHkOQjNLnoPQzJLnIDSz5DkIzSx5/x/wgbu3KUQYlAAAAABJRU5ErkJggg==\n",
|
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"text/plain": [
|
|
"\u003cFigure size 360x360 with 1 Axes\u003e"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light",
|
|
"tags": []
|
|
},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
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"data": {
|
|
"image/png": "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\n",
|
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"text/plain": [
|
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"\u003cFigure size 360x360 with 1 Axes\u003e"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light",
|
|
"tags": []
|
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},
|
|
"output_type": "display_data"
|
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}
|
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],
|
|
"source": [
|
|
"#@title Train affordance and transition model.\n",
|
|
"# Trains num_repeats affordance and model networks.\n",
|
|
"#@markdown Experiments to run.\n",
|
|
"run_model = True #@param {type:\"boolean\"}\n",
|
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"run_model_with_affordances = True #@param {type:\"boolean\"}\n",
|
|
"num_repeats = 1#@param {type:\"integer\"}\n",
|
|
"\n",
|
|
"#@markdown Training arguments\n",
|
|
"# use_affordance_to_mask_model = True #@param {type:\"boolean\"}\n",
|
|
"optimize_performance = True #@param {type:\"boolean\"}\n",
|
|
"model_learning_rate = 1e-2 #@param {type:\"number\"}\n",
|
|
"affordance_learning_rate = 1e-1 #@param {type:\"number\"}\n",
|
|
"max_num_transitions = 1000 #@param {type:\"integer\"}\n",
|
|
"num_train_steps = 8000 #@param {type:\"integer\"}\n",
|
|
"affordance_mask_threshold = 0.5 #@param {type:\"number\"}\n",
|
|
"seed = 0 #@param {type:\"integer\"}\n",
|
|
"intent_threshold = 0.05 #@param {type:\"number\"}\n",
|
|
"\n",
|
|
"#@markdown Environment arguments\n",
|
|
"drift_speed = 0.001 #@param {type:\"number\"}\n",
|
|
"max_action_force = 0.5 #@param {type:\"number\"}\n",
|
|
"movement_noise = 0.1 #@param {type:\"number\"}\n",
|
|
"max_episode_length = 100000 #@param {type:\"integer\"}\n",
|
|
"\n",
|
|
"input_size = 2\n",
|
|
"action_size = 2\n",
|
|
"intent_size = len(IntentName)\n",
|
|
"hidden_nodes = 32\n",
|
|
"world_size = 2\n",
|
|
"\n",
|
|
"affordance_mask_params = []\n",
|
|
"if run_model:\n",
|
|
" affordance_mask_params.append(False)\n",
|
|
"if run_model_with_affordances:\n",
|
|
" affordance_mask_params.append(True)\n",
|
|
"\n",
|
|
"print(f'Experiments that will be run: {affordance_mask_params}')\n",
|
|
"\n",
|
|
"for repeat_number in range(num_repeats):\n",
|
|
" all_losses = {}\n",
|
|
" model_networks = {}\n",
|
|
" affordance_networks = {}\n",
|
|
" new_seed = seed + repeat_number\n",
|
|
" for use_affordance_to_mask_model in affordance_mask_params:\n",
|
|
" print(f'Resetting seed to {new_seed}.')\n",
|
|
" np.random.seed(new_seed)\n",
|
|
" random.seed(new_seed)\n",
|
|
" tf.random.set_seed(new_seed)\n",
|
|
"\n",
|
|
" affordance_network = tf.keras.Sequential([\n",
|
|
" tf.keras.layers.Dense(\n",
|
|
" hidden_nodes, activation=tf.keras.activations.relu),\n",
|
|
" tf.keras.layers.Dense(\n",
|
|
" hidden_nodes, activation=tf.keras.activations.relu),\n",
|
|
" tf.keras.layers.Dense(\n",
|
|
" intent_size, activation=tf.keras.activations.sigmoid),\n",
|
|
" ])\n",
|
|
"\n",
|
|
" affordance_sgd = tf.keras.optimizers.Adam(\n",
|
|
" learning_rate=affordance_learning_rate)\n",
|
|
" model_sgd = tf.keras.optimizers.Adam(learning_rate=model_learning_rate)\n",
|
|
" model_network = TransitionModel(hidden_nodes, input_size)\n",
|
|
"\n",
|
|
" # Store models for later use.\n",
|
|
" model_networks[use_affordance_to_mask_model] = model_network\n",
|
|
" affordance_networks[use_affordance_to_mask_model] = affordance_network\n",
|
|
"\n",
|
|
" world = ContinuousWorld(\n",
|
|
" size=world_size,\n",
|
|
" # Slow drift speed to make the transition from L -\u003e R slow.\n",
|
|
" drift_speed=drift_speed,\n",
|
|
" drift_between=(\n",
|
|
" # Drift between the two sides around the wall.\n",
|
|
" Point((1 / 4) * world_size, (1 / 4) * world_size),\n",
|
|
" Point((3 / 4) * world_size, (3 / 4) * world_size),\n",
|
|
" ),\n",
|
|
" max_action_force=max_action_force,\n",
|
|
" max_episode_length=max_episode_length,\n",
|
|
" movement_noise=movement_noise,\n",
|
|
" wall_pairs=[\n",
|
|
" (Point(1.0, 0.0), Point(1.0, 2.0)),\n",
|
|
" ],\n",
|
|
" verbose_reset=True)\n",
|
|
"\n",
|
|
" fig = plt.figure(figsize=(5, 5))\n",
|
|
" ax = fig.add_subplot(1, 1, 1)\n",
|
|
"\n",
|
|
" visualize_environment(\n",
|
|
" world, ax, scaling=1.0, draw_start_mu=False, draw_target_mu=False)\n",
|
|
"\n",
|
|
" def _use_affordance_or_none(model):\n",
|
|
" if use_affordance_to_mask_model:\n",
|
|
" return model\n",
|
|
" else:\n",
|
|
" return None\n",
|
|
"\n",
|
|
" model_loss, aff_loss, infos = train_networks(\n",
|
|
" world,\n",
|
|
" model_network=model_network,\n",
|
|
" model_optimizer=model_sgd,\n",
|
|
" affordance_network=_use_affordance_or_none(affordance_network),\n",
|
|
" affordance_optimizer=_use_affordance_or_none(affordance_sgd),\n",
|
|
" print_losses=True,\n",
|
|
" fresh_data=True,\n",
|
|
" affordance_mask_threshold=affordance_mask_threshold,\n",
|
|
" use_affordance_to_mask_model=use_affordance_to_mask_model,\n",
|
|
" max_num_transitions=max_num_transitions,\n",
|
|
" max_trajectory_length=None,\n",
|
|
" optimize_performance=optimize_performance,\n",
|
|
" num_train_steps=num_train_steps,\n",
|
|
" intent_threshold=intent_threshold,\n",
|
|
" print_every=1000)\n",
|
|
"\n",
|
|
" all_losses[use_affordance_to_mask_model] = (model_loss, aff_loss, infos)\n",
|
|
"\n",
|
|
" all_models_global.append(model_networks)\n",
|
|
" all_affordance_global.append(affordance_networks)\n",
|
|
" all_losses_global.append(all_losses)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 0,
|
|
"metadata": {
|
|
"cellView": "form",
|
|
"colab": {},
|
|
"colab_type": "code",
|
|
"id": "90KLuKumiRcB"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"#@title Save weights\n",
|
|
"\n",
|
|
"for seed, model_networks in enumerate(all_models_global):\n",
|
|
" model_networks[True].save_weights(\n",
|
|
" f'./affordances/seed_{seed}_model_networks_True/keras.weights')\n",
|
|
" model_networks[False].save_weights(\n",
|
|
" f'./affordances/seed_{seed}_model_networks_False/keras.weights')\n",
|
|
"for seed, affordance_networks in enumerate(all_models_global):\n",
|
|
" affordance_networks[True].save_weights(\n",
|
|
" f'./affordances/seed_{seed}_affordance_networks_true/keras.weights')\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"colab_type": "text",
|
|
"id": "g1iO3h6Ffucu"
|
|
},
|
|
"source": [
|
|
"# Visualizations\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"colab_type": "text",
|
|
"id": "OQtxS_ovhJxt"
|
|
},
|
|
"source": [
|
|
"## Learning curves"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 0,
|
|
"metadata": {
|
|
"cellView": "form",
|
|
"colab": {},
|
|
"colab_type": "code",
|
|
"id": "06NPUpXDfyFf"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"#@title Code to collect results from a list of lists into a single array.\n",
|
|
"def _collect_results(\n",
|
|
" all_losses_g,\n",
|
|
" using_affordances,\n",
|
|
" save_to_disk=False,\n",
|
|
" smooth_weight=0.99,\n",
|
|
" skip_first=10):\n",
|
|
" \"\"\"Collects results from the list of losses.\"\"\"\n",
|
|
" smoothed_curves = []\n",
|
|
" for seed, trace in enumerate(all_losses_g):\n",
|
|
" if save_to_disk:\n",
|
|
" np.save(f'./affordances/curve_seed_{seed}_{using_affordances}.npy',\n",
|
|
" np.array(trace[using_affordances][0]))\n",
|
|
" # Smooth the curves for plotting.\n",
|
|
" smoothed_curves.append(\n",
|
|
" apply_linear_smoothing(\n",
|
|
" trace[using_affordances][0][skip_first:], smooth_weight))\n",
|
|
" all_curves_stacked = np.stack(smoothed_curves)\n",
|
|
" mean_curve = np.mean(all_curves_stacked, 0)\n",
|
|
" std_curve = np.std(all_curves_stacked, 0)\n",
|
|
" return mean_curve, std_curve"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 0,
|
|
"metadata": {
|
|
"cellView": "form",
|
|
"colab": {},
|
|
"colab_type": "code",
|
|
"id": "R4elUiamgc-I"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"#@title Plot averaged learning curves.\n",
|
|
"\n",
|
|
"mean_curve_aff, std_curve_aff = _collect_results(\n",
|
|
" all_losses_global, True)\n",
|
|
"mean_curve_normal, std_curve_normal = _collect_results(\n",
|
|
" all_losses_global, False)\n",
|
|
"colors = ['r', 'k']\n",
|
|
"plt.plot(\n",
|
|
" mean_curve_aff, color=colors[0], linewidth=4, label='With Affordance')\n",
|
|
"plt.plot(\n",
|
|
" mean_curve_normal, color=colors[1], linewidth=4, label='without Affordance')\n",
|
|
"\n",
|
|
"plt.fill_between(\n",
|
|
" range(len(mean_curve_aff)),\n",
|
|
" mean_curve_aff+std_curve_aff,\n",
|
|
" mean_curve_aff-std_curve_aff,\n",
|
|
" alpha=0.25,\n",
|
|
" color=colors[0])\n",
|
|
"\n",
|
|
"\n",
|
|
"plt.fill_between(\n",
|
|
" range(len(mean_curve_normal)),\n",
|
|
" mean_curve_normal+std_curve_normal/np.sqrt(num_repeats),\n",
|
|
" mean_curve_normal-std_curve_normal/np.sqrt(num_repeats),\n",
|
|
" alpha=0.25,\n",
|
|
" color=colors[1])\n",
|
|
"\n",
|
|
"plt.ylim([-2.2, -1.2])\n",
|
|
"plt.xticks(fontsize=15)\n",
|
|
"plt.yticks(fontsize=15)\n",
|
|
"plt.xticks([0, 2500, 5000, 7500],[0, 2500, 5000, 7500], fontsize=15)\n",
|
|
"plt.legend(fontsize=15)\n",
|
|
"plt.xlabel('Updates', fontsize=20)\n",
|
|
"plt.ylabel(r'$-\\log \\hat{P}(s^\\prime|s,a)$',fontsize=20)\n",
|
|
"plt.tight_layout()\n",
|
|
"plt.savefig('./affordances/model_learning_avg_plot.pdf')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"colab_type": "text",
|
|
"id": "OSAm0ukchLqr"
|
|
},
|
|
"source": [
|
|
"## Intent heatmap plots"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 0,
|
|
"metadata": {
|
|
"cellView": "form",
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/",
|
|
"height": 399
|
|
},
|
|
"colab_type": "code",
|
|
"id": "1NeTHiZkhXKj",
|
|
"outputId": "2428756a-8685-4312-f3f3-1bf4be01188b"
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:56: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": 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\n",
|
|
"text/plain": [
|
|
"\u003cFigure size 360x360 with 5 Axes\u003e"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light",
|
|
"tags": []
|
|
},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"#@title Evaluating action and plotting intent heatmaps.\n",
|
|
"#@markdown What is the action?\n",
|
|
"action_x_dir = 0.2 #@param {type:\"number\"}\n",
|
|
"action_y_dir = 0.2 #@param {type:\"number\"}\n",
|
|
"network_seed = 0 #@param {type:\"integer\"}\n",
|
|
"\n",
|
|
"# Cover the x-y grid.\n",
|
|
"xs = np.linspace(0, world.size)\n",
|
|
"ys = np.linspace(0, world.size)\n",
|
|
"xy_coords = tf.constant(list(itertools.product(xs, ys)), dtype=tf.float32)\n",
|
|
"\n",
|
|
"eval_action = [action_x_dir, action_y_dir]\n",
|
|
"fixed_action = tf.constant([eval_action], dtype=tf.float32)\n",
|
|
"fixed_action = tf.repeat(fixed_action, 2500, axis=0)\n",
|
|
"\n",
|
|
"concat_matrix = tf.concat((xy_coords, fixed_action), axis=1)\n",
|
|
"affordance_network = all_affordance_global[network_seed][True]\n",
|
|
"afford_predictions = affordance_network(concat_matrix)\n",
|
|
"affordance_predictions = tf.reshape(\n",
|
|
" afford_predictions,\n",
|
|
" (len(xs), len(ys), intent_size)).numpy()\n",
|
|
"\n",
|
|
"plot_intents(world, affordance_predictions, eval_action)\n",
|
|
"\n",
|
|
"plt.savefig(\n",
|
|
" f'intent_eval_FX{action_x_dir}_FY{action_y_dir}.pdf', bbox_inches='tight')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"colab_type": "text",
|
|
"id": "mOK_e3oqhrjI"
|
|
},
|
|
"source": [
|
|
"## Model Predictions"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 0,
|
|
"metadata": {
|
|
"cellView": "form",
|
|
"colab": {},
|
|
"colab_type": "code",
|
|
"id": "61Gb_tXKh3je"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"#@title Round annotation plotting code.\n",
|
|
"ROUND_BOX = dict(boxstyle='round', facecolor='wheat', alpha=1.0)\n",
|
|
"\n",
|
|
"def add_annotation(\n",
|
|
" ax,\n",
|
|
" start: Tuple[float, float],\n",
|
|
" end: Tuple[float, float],\n",
|
|
" connectionstyle, text):\n",
|
|
" x1, y1 = start\n",
|
|
" x2, y2 = end\n",
|
|
"\n",
|
|
" # ax.plot([x1, x2], [y1, y2], \".\")\n",
|
|
" ax.annotate(\n",
|
|
" \"\",\n",
|
|
" xy=(x1, y1),\n",
|
|
" xycoords='data',\n",
|
|
" xytext=(x2 + 0.25, y2),\n",
|
|
" textcoords='data',\n",
|
|
" size=30.0,\n",
|
|
" arrowprops=dict(arrowstyle=\"-\u003e\", color=\"0.0\",\n",
|
|
" shrinkA=5, shrinkB=5,\n",
|
|
" patchA=None, patchB=None,\n",
|
|
" connectionstyle=connectionstyle,),)\n",
|
|
"\n",
|
|
" ax.text(*end, text, size=15,\n",
|
|
" #transform=ax.transAxes,\n",
|
|
" ha=\"left\", va=\"top\", bbox=ROUND_BOX)\n",
|
|
"\n",
|
|
"connection_styles = [\n",
|
|
" \"arc3,rad=-0.3\",\n",
|
|
" \"arc3,rad=0.3\",\n",
|
|
" \"arc3,rad=0.0\",\n",
|
|
" \"arc3,rad=0.5\",\n",
|
|
" \"arc3,rad=-0.5\"\n",
|
|
"]"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 0,
|
|
"metadata": {
|
|
"cellView": "both",
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/",
|
|
"height": 689
|
|
},
|
|
"colab_type": "code",
|
|
"id": "ILHEEbHShwCb",
|
|
"outputId": "9bac0134-d9d1-46da-9283-975ede52250e"
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Figures show the predicted position of the transition distribution.\n",
|
|
"Gray circle shows what would have been predicted but was masked by affordance model. \n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": 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\n",
|
|
"text/plain": [
|
|
"\u003cFigure size 360x360 with 1 Axes\u003e"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light",
|
|
"tags": []
|
|
},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"data": {
|
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"image/png": 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\n",
|
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"text/plain": [
|
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"\u003cFigure size 360x360 with 1 Axes\u003e"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light",
|
|
"tags": []
|
|
},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
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"source": [
|
|
"#@title Plotting Model predictions.\n",
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"\n",
|
|
"#@markdown Use affordance based model?\n",
|
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"affordance_mask_threshold = 0.5 #@param {type:\"number\"}\n",
|
|
"network_seed = 0 #@param {type:\"integer\"}\n",
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"\n",
|
|
"#@markdown What is the action?\n",
|
|
"action_x_dir = +0.5 #@param {type:\"number\"}\n",
|
|
"action_y_dir = 0.0 #@param {type:\"number\"}\n",
|
|
"\n",
|
|
"#@markdown Where is the agent?\n",
|
|
"agent_x = 0.75 #@param {type:\"number\"}\n",
|
|
"agent_y = 1.0 #@param {type:\"number\"}\n",
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|
"\n",
|
|
"action = tf.constant([[action_x_dir, action_y_dir]])\n",
|
|
"pos = tf.constant([[agent_x, agent_y]])\n",
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|
"\n",
|
|
"affordance_networks = all_affordance_global[network_seed]\n",
|
|
"model_networks = all_models_global[network_seed]\n",
|
|
"\n",
|
|
"scale_scale = 2.0\n",
|
|
"\n",
|
|
"for i, use_affordance_to_mask_model in enumerate([False, True]):\n",
|
|
" fig = plt.figure(figsize=(5, 5))\n",
|
|
" ax = fig.add_subplot(1, 1, 1)\n",
|
|
" transition_dist = model_networks[use_affordance_to_mask_model](pos, action)\n",
|
|
" transition_loc = tuple(transition_dist.loc[0].numpy())\n",
|
|
" transition_scale = tuple(transition_dist.scale[0].numpy() * scale_scale)\n",
|
|
"\n",
|
|
" if use_affordance_to_mask_model:\n",
|
|
" aff_network = affordance_networks[use_affordance_to_mask_model]\n",
|
|
" AF = aff_network(tf.concat([pos, action], axis=1))\n",
|
|
" intents_completable = (AF \u003e affordance_mask_threshold)[0].numpy()\n",
|
|
"\n",
|
|
" visualize_environment(\n",
|
|
" world,\n",
|
|
" ax,\n",
|
|
" scaling=1.0,\n",
|
|
" draw_start_mu=False,\n",
|
|
" draw_target_mu=False,\n",
|
|
" draw_agent=False,\n",
|
|
" agent_size=0.1,\n",
|
|
" write_text=False)\n",
|
|
" ax.scatter([agent_x], [agent_y], s=150.0, c='green', marker='x')\n",
|
|
" ax.arrow(agent_x, agent_y, action_x_dir, action_y_dir, head_width=0.05)\n",
|
|
"\n",
|
|
" if use_affordance_to_mask_model and not np.any(intents_completable):\n",
|
|
" color = 'gray'\n",
|
|
" alpha = 0.25\n",
|
|
" ellipse_text = '(Masked) '\n",
|
|
" else:\n",
|
|
" color = None\n",
|
|
" alpha = 0.7\n",
|
|
" ellipse_text = ''\n",
|
|
"\n",
|
|
" elipse = mpl.patches.Ellipse(\n",
|
|
" transition_loc, *transition_scale, alpha=alpha, color=color)\n",
|
|
" ax.add_artist(elipse)\n",
|
|
"\n",
|
|
" if use_affordance_to_mask_model:\n",
|
|
" string_built = ' Intent classificaiton\\n'\n",
|
|
" for a in list(zip(IntentName, intents_completable)):\n",
|
|
" string_built += ' ' + str(a[0])[-5:] + ':' + str(a[1])\n",
|
|
" string_built += '\\n'\n",
|
|
" ax.text(\n",
|
|
" 0,\n",
|
|
" 0,\n",
|
|
" string_built,\n",
|
|
" )\n",
|
|
"\n",
|
|
" ax.set_xticks([0.0, 1.0, 2.0])\n",
|
|
" ax.set_xticklabels([0, 1.0, 2.0])\n",
|
|
"\n",
|
|
" ax.set_yticks([0.0, 1.0, 2.0])\n",
|
|
" ax.set_yticklabels([0, 1.0, 2.0])\n",
|
|
"\n",
|
|
" if use_affordance_to_mask_model:\n",
|
|
" title = 'Using affordances'\n",
|
|
" else:\n",
|
|
" title = 'Without affordances'\n",
|
|
" ax.set_title(title)\n",
|
|
" ax.legend([elipse], [ellipse_text + 'Predicted transition'])\n",
|
|
" file_name = (f'./empirical_demo{movement_noise}_P{agent_x}_{agent_y}_'\n",
|
|
" f'F{action_x_dir}_{action_x_dir}.pdf')\n",
|
|
" fig.savefig(file_name)\n",
|
|
"\n",
|
|
"print(\n",
|
|
" 'Figures show the predicted position of the transition distribution.'\n",
|
|
" '\\nGray circle shows what would have been predicted but was masked by '\n",
|
|
" 'affordance model. ')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 0,
|
|
"metadata": {
|
|
"colab": {},
|
|
"colab_type": "code",
|
|
"id": "5GHbTbNClfL-"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
""
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"colab": {
|
|
"collapsed_sections": [],
|
|
"name": "AffordancesInContinuousEnvironment.ipynb",
|
|
"provenance": [
|
|
{
|
|
"file_id": "1W86NFSHwhnx-UEmAY_mhJJzUxPXY3JC4",
|
|
"timestamp": 1591715576521
|
|
}
|
|
]
|
|
},
|
|
"kernelspec": {
|
|
"display_name": "Python 3",
|
|
"name": "python3"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 0
|
|
}
|