mirror of
https://github.com/google-deepmind/deepmind-research.git
synced 2026-05-09 21:07:49 +08:00
Shaobo Hou
60550a5bc6
Export training curves to file and fix some inconsistencies. PiperOrigin-RevId: 324825810
71 lines
2.2 KiB
Python
71 lines
2.2 KiB
Python
# Lint as: python3
|
|
# pylint: disable=g-bad-file-header
|
|
# Copyright 2020 DeepMind Technologies Limited. All Rights Reserved.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
# ============================================================================
|
|
"""Run an experiment."""
|
|
|
|
from absl import app
|
|
from absl import flags
|
|
|
|
import tensorflow.compat.v1 as tf
|
|
|
|
from option_keyboard import configs
|
|
from option_keyboard import dqn_agent
|
|
from option_keyboard import environment_wrappers
|
|
from option_keyboard import experiment
|
|
from option_keyboard import scavenger
|
|
|
|
FLAGS = flags.FLAGS
|
|
flags.DEFINE_integer("num_episodes", 10000, "Number of training episodes.")
|
|
flags.DEFINE_integer("report_every", 200,
|
|
"Frequency at which metrics are reported.")
|
|
flags.DEFINE_string("output_path", None, "Path to write out training curves.")
|
|
|
|
|
|
def main(argv):
|
|
del argv
|
|
|
|
# Create the task environment.
|
|
env_config = configs.get_task_config()
|
|
env = scavenger.Scavenger(**env_config)
|
|
env = environment_wrappers.EnvironmentWithLogging(env)
|
|
|
|
# Create the flat agent.
|
|
agent = dqn_agent.Agent(
|
|
obs_spec=env.observation_spec(),
|
|
action_spec=env.action_spec(),
|
|
network_kwargs=dict(
|
|
output_sizes=(64, 128),
|
|
activate_final=True,
|
|
),
|
|
epsilon=0.1,
|
|
additional_discount=0.9,
|
|
batch_size=10,
|
|
optimizer_name="AdamOptimizer",
|
|
optimizer_kwargs=dict(learning_rate=3e-4,))
|
|
|
|
_, ema_returns = experiment.run(
|
|
env,
|
|
agent,
|
|
num_episodes=FLAGS.num_episodes,
|
|
report_every=FLAGS.report_every)
|
|
if FLAGS.output_path:
|
|
experiment.write_returns_to_file(FLAGS.output_path, ema_returns)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
tf.disable_v2_behavior()
|
|
app.run(main)
|