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https://github.com/google-deepmind/deepmind-research.git
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Shaobo Hou
93 lines
3.0 KiB
Python
93 lines
3.0 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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"""Run an experiment."""
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import os
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from absl import app
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from absl import flags
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import tensorflow.compat.v1 as tf
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import tensorflow_hub as hub
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from option_keyboard import configs
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from option_keyboard import dqn_agent
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from option_keyboard import environment_wrappers
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from option_keyboard import experiment
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from option_keyboard import keyboard_utils
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from option_keyboard import scavenger
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from option_keyboard import smart_module
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FLAGS = flags.FLAGS
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flags.DEFINE_integer("num_episodes", 10000, "Number of training episodes.")
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flags.DEFINE_integer("num_pretrain_episodes", 20000,
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"Number of pretraining episodes.")
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flags.DEFINE_string("keyboard_path", None, "Path to pretrained keyboard model.")
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def main(argv):
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del argv
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# Pretrain the keyboard and save a checkpoint.
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if FLAGS.keyboard_path:
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keyboard_path = FLAGS.keyboard_path
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else:
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with tf.Graph().as_default():
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export_path = "/tmp/option_keyboard/keyboard"
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_ = keyboard_utils.create_and_train_keyboard(
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num_episodes=FLAGS.num_pretrain_episodes, export_path=export_path)
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keyboard_path = os.path.join(export_path, "tfhub")
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# Load the keyboard.
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keyboard = smart_module.SmartModuleImport(hub.Module(keyboard_path))
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# Create the task environment.
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base_env_config = configs.get_task_config()
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base_env = scavenger.Scavenger(**base_env_config)
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base_env = environment_wrappers.EnvironmentWithLogging(base_env)
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# Wrap the task environment with the keyboard.
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additional_discount = 0.9
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env = environment_wrappers.EnvironmentWithKeyboard(
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env=base_env,
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keyboard=keyboard,
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keyboard_ckpt_path=None,
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n_actions_per_dim=3,
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additional_discount=additional_discount,
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call_and_return=False)
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# Create the player agent.
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agent = dqn_agent.Agent(
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obs_spec=env.observation_spec(),
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action_spec=env.action_spec(),
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network_kwargs=dict(
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output_sizes=(64, 128),
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activate_final=True,
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),
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epsilon=0.1,
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additional_discount=additional_discount,
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batch_size=10,
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optimizer_name="AdamOptimizer",
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optimizer_kwargs=dict(learning_rate=3e-4,))
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experiment.run(env, agent, num_episodes=FLAGS.num_episodes)
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if __name__ == "__main__":
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tf.disable_v2_behavior()
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app.run(main)
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