Release of option_keyboard code.

PiperOrigin-RevId: 310175317
2026-05-30 04:05:27 +08:00 · 2020-05-06 18:10:44 +01:00
parent 6f14cb5983
commit 391bc47f3c
15 changed files with 1342 additions and 0 deletions
@@ -0,0 +1,108 @@
+# 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 numpy as np
+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 keyboard_agent
+from option_keyboard import scavenger
+
+FLAGS = flags.FLAGS
+flags.DEFINE_integer("num_episodes", 10000, "Number of training episodes.")
+flags.DEFINE_integer("num_pretrain_episodes", 20000,
+                     "Number of pretraining episodes.")
+
+
+def _train_keyboard(num_episodes):
+  """Train an option keyboard."""
+  env_config = configs.get_pretrain_config()
+  env = scavenger.Scavenger(**env_config)
+  env = environment_wrappers.EnvironmentWithLogging(env)
+
+  agent = keyboard_agent.Agent(
+      obs_spec=env.observation_spec(),
+      action_spec=env.action_spec(),
+      policy_weights=np.array([
+          [1.0, 0.0],
+          [0.0, 1.0],
+      ]),
+      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,))
+
+  experiment.run(env, agent, num_episodes=num_episodes)
+
+  return agent
+
+
+def main(argv):
+  del argv
+
+  # Pretrain the keyboard and save a checkpoint.
+  pretrain_agent = _train_keyboard(num_episodes=FLAGS.num_pretrain_episodes)
+  keyboard_ckpt_path = "/tmp/option_keyboard/keyboard.ckpt"
+  pretrain_agent.export(keyboard_ckpt_path)
+
+  # Create the task environment.
+  base_env_config = configs.get_task_config()
+  base_env = scavenger.Scavenger(**base_env_config)
+  base_env = environment_wrappers.EnvironmentWithLogging(base_env)
+
+  # Wrap the task environment with the keyboard.
+  additional_discount = 0.9
+  env = environment_wrappers.EnvironmentWithKeyboard(
+      env=base_env,
+      keyboard=pretrain_agent.keyboard,
+      keyboard_ckpt_path=keyboard_ckpt_path,
+      n_actions_per_dim=3,
+      additional_discount=additional_discount,
+      call_and_return=True)
+
+  # Create the player 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=additional_discount,
+      batch_size=10,
+      optimizer_name="AdamOptimizer",
+      optimizer_kwargs=dict(learning_rate=3e-4,))
+
+  experiment.run(env, agent, num_episodes=FLAGS.num_episodes)
+
+
+if __name__ == "__main__":
+  tf.disable_v2_behavior()
+  app.run(main)