mirror of
https://github.com/google-deepmind/deepmind-research.git
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Shaobo Hou
360 lines
11 KiB
Python
360 lines
11 KiB
Python
# Lint as: python3
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# pylint: disable=g-bad-file-header
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# Copyright 2020 DeepMind Technologies Limited. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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"""Environment with keyboard."""
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import itertools
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from absl import logging
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import dm_env
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import numpy as np
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import tensorflow.compat.v1 as tf
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import tensorflow_hub as hub
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import tree
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from option_keyboard import smart_module
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class EnvironmentWithLogging(dm_env.Environment):
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"""Wraps an environment with additional logging."""
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def __init__(self, env):
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self._env = env
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self._episode_return = 0
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def reset(self):
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self._episode_return = 0
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return self._env.reset()
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def step(self, action):
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"""Take action in the environment and do some logging."""
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step = self._env.step(action)
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if step.first():
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step = self._env.step(action)
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self._episode_return = 0
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self._episode_return += step.reward
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return step
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@property
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def episode_return(self):
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return self._episode_return
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def action_spec(self):
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return self._env.action_spec()
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def observation_spec(self):
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return self._env.observation_spec()
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def __getattr__(self, name):
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return getattr(self._env, name)
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class EnvironmentWithKeyboard(dm_env.Environment):
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"""Wraps an environment with a keyboard."""
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def __init__(self,
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env,
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keyboard,
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keyboard_ckpt_path,
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n_actions_per_dim,
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additional_discount,
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call_and_return=False):
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self._env = env
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self._keyboard = keyboard
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self._discount = additional_discount
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self._call_and_return = call_and_return
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options = _discretize_actions(n_actions_per_dim, keyboard.num_cumulants)
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self._options_np = options
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options = tf.convert_to_tensor(options, dtype=tf.float32)
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self._options = options
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obs_spec = self._extract_observation(env.observation_spec())
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obs_ph = tf.placeholder(shape=obs_spec.shape, dtype=obs_spec.dtype)
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option_ph = tf.placeholder(shape=(), dtype=tf.int32)
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gpi_action = self._keyboard.gpi(obs_ph, options[option_ph])
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session = tf.Session()
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self._gpi_action = session.make_callable(gpi_action, [obs_ph, option_ph])
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self._keyboard_action = session.make_callable(
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self._keyboard(tf.expand_dims(obs_ph, axis=0))[0], [obs_ph])
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session.run(tf.global_variables_initializer())
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if keyboard_ckpt_path:
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saver = tf.train.Saver(var_list=keyboard.variables)
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saver.restore(session, keyboard_ckpt_path)
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def _compute_reward(self, option, obs):
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return np.sum(self._options_np[option] * obs["cumulants"])
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def reset(self):
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return self._env.reset()
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def step(self, option):
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"""Take a step in the keyboard, then the environment."""
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step_count = 0
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option_step = None
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while True:
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obs = self._extract_observation(self._env.observation())
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action = self._gpi_action(obs, option)
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action_step = self._env.step(action)
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step_count += 1
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if option_step is None:
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option_step = action_step
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else:
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new_discount = (
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option_step.discount * self._discount * action_step.discount)
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new_reward = (
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option_step.reward + new_discount * action_step.reward)
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option_step = option_step._replace(
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observation=action_step.observation,
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reward=new_reward,
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discount=new_discount,
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step_type=action_step.step_type)
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if action_step.last():
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break
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# Terminate option.
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if self._compute_reward(option, action_step.observation) > 0:
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break
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if not self._call_and_return:
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break
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return option_step
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def action_spec(self):
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return dm_env.specs.DiscreteArray(
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num_values=self._options_np.shape[0], name="action")
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def _extract_observation(self, obs):
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return obs["arena"]
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def observation_spec(self):
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return self._env.observation_spec()
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def __getattr__(self, name):
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return getattr(self._env, name)
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class EnvironmentWithKeyboardDirect(dm_env.Environment):
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"""Wraps an environment with a keyboard.
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This is different from EnvironmentWithKeyboard as the actions space is not
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discretized.
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TODO(shaobohou) Merge the two implementations.
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"""
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def __init__(self,
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env,
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keyboard,
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keyboard_ckpt_path,
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additional_discount,
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call_and_return=False):
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self._env = env
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self._keyboard = keyboard
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self._discount = additional_discount
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self._call_and_return = call_and_return
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obs_spec = self._extract_observation(env.observation_spec())
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obs_ph = tf.placeholder(shape=obs_spec.shape, dtype=obs_spec.dtype)
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option_ph = tf.placeholder(
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shape=(keyboard.num_cumulants,), dtype=tf.float32)
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gpi_action = self._keyboard.gpi(obs_ph, option_ph)
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session = tf.Session()
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self._gpi_action = session.make_callable(gpi_action, [obs_ph, option_ph])
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self._keyboard_action = session.make_callable(
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self._keyboard(tf.expand_dims(obs_ph, axis=0))[0], [obs_ph])
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session.run(tf.global_variables_initializer())
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if keyboard_ckpt_path:
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saver = tf.train.Saver(var_list=keyboard.variables)
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saver.restore(session, keyboard_ckpt_path)
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def _compute_reward(self, option, obs):
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assert option.shape == obs["cumulants"].shape
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return np.sum(option * obs["cumulants"])
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def reset(self):
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return self._env.reset()
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def step(self, option):
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"""Take a step in the keyboard, then the environment."""
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step_count = 0
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option_step = None
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while True:
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obs = self._extract_observation(self._env.observation())
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action = self._gpi_action(obs, option)
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action_step = self._env.step(action)
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step_count += 1
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if option_step is None:
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option_step = action_step
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else:
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new_discount = (
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option_step.discount * self._discount * action_step.discount)
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new_reward = (
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option_step.reward + new_discount * action_step.reward)
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option_step = option_step._replace(
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observation=action_step.observation,
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reward=new_reward,
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discount=new_discount,
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step_type=action_step.step_type)
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if action_step.last():
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break
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# Terminate option.
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if self._compute_reward(option, action_step.observation) > 0:
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break
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if not self._call_and_return:
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break
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return option_step
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def action_spec(self):
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return dm_env.specs.BoundedArray(shape=(self._keyboard.num_cumulants,),
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dtype=np.float32,
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minimum=-1.0,
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maximum=1.0,
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name="action")
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def _extract_observation(self, obs):
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return obs["arena"]
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def observation_spec(self):
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return self._env.observation_spec()
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def __getattr__(self, name):
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return getattr(self._env, name)
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def _discretize_actions(num_actions_per_dim,
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action_space_dim,
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min_val=-1.0,
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max_val=1.0):
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"""Discrete action space."""
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if num_actions_per_dim > 1:
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discretized_dim_action = np.linspace(
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min_val, max_val, num_actions_per_dim, endpoint=True)
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discretized_actions = [discretized_dim_action] * action_space_dim
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discretized_actions = itertools.product(*discretized_actions)
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discretized_actions = list(discretized_actions)
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elif num_actions_per_dim == 1:
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discretized_actions = [
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max_val * np.eye(action_space_dim),
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min_val * np.eye(action_space_dim),
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]
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discretized_actions = np.concatenate(discretized_actions, axis=0)
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elif num_actions_per_dim == 0:
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discretized_actions = np.eye(action_space_dim)
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else:
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raise ValueError(
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"Unsupported num_actions_per_dim {}".format(num_actions_per_dim))
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discretized_actions = np.array(discretized_actions)
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# Remove options with all zeros.
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non_zero_entries = np.sum(np.square(discretized_actions), axis=-1) != 0.0
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# Remove options with no positive elements.
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non_negative_entries = np.any(discretized_actions > 0, axis=-1)
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discretized_actions = discretized_actions[np.logical_and(
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non_zero_entries, non_negative_entries)]
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logging.info("Total number of discretized actions: %s",
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len(discretized_actions))
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logging.info("Discretized actions: %s", discretized_actions)
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return discretized_actions
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class EnvironmentWithLearnedPhi(dm_env.Environment):
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"""Wraps an environment with learned phi model."""
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def __init__(self, env, model_path):
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self._env = env
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create_ph = lambda x: tf.placeholder(shape=x.shape, dtype=x.dtype)
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add_batch = lambda x: tf.expand_dims(x, axis=0)
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# Make session and callables.
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with tf.Graph().as_default():
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model = smart_module.SmartModuleImport(hub.Module(model_path))
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obs_spec = env.observation_spec()
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obs_ph = tree.map_structure(create_ph, obs_spec)
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action_ph = tf.placeholder(shape=(), dtype=tf.int32)
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phis = model(tree.map_structure(add_batch, obs_ph), add_batch(action_ph))
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self.num_phis = phis.shape.as_list()[-1]
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self._last_phis = np.zeros((self.num_phis,), dtype=np.float32)
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session = tf.Session()
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self._session = session
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self._phis_fn = session.make_callable(
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phis[0], tree.flatten([obs_ph, action_ph]))
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self._session.run(tf.global_variables_initializer())
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def reset(self):
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self._last_phis = np.zeros((self.num_phis,), dtype=np.float32)
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return self._env.reset()
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def step(self, action):
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"""Take action in the environment and do some logging."""
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phis = self._phis_fn(*tree.flatten([self._env.observation(), action]))
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step = self._env.step(action)
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if step.first():
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phis = self._phis_fn(*tree.flatten([self._env.observation(), action]))
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step = self._env.step(action)
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step.observation["cumulants"] = phis
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self._last_phis = phis
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return step
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def action_spec(self):
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return self._env.action_spec()
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def observation(self):
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obs = self._env.observation()
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obs["cumulants"] = self._last_phis
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return obs
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def observation_spec(self):
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obs_spec = self._env.observation_spec()
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obs_spec["cumulants"] = dm_env.specs.BoundedArray(
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shape=(self.num_phis,),
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dtype=np.float32,
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minimum=-1e9,
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maximum=1e9,
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name="collected_resources")
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return obs_spec
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def __getattr__(self, name):
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return getattr(self._env, name)
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