def main(argv): del argv # Create the task environment. env_config = configs.get_fig4_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)
def main(argv): del argv # Load the keyboard. keyboard = smart_module.SmartModuleImport(hub.Module(FLAGS.keyboard_path)) # Create the task environment. base_env_config = configs.get_fig4_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.EnvironmentWithKeyboardDirect( env=base_env, keyboard=keyboard, keyboard_ckpt_path=None, additional_discount=additional_discount, call_and_return=False) # Create the player agent. agent = regressed_agent.Agent( batch_size=10, optimizer_name="AdamOptimizer", optimizer_kwargs=dict(learning_rate=3e-2, ), init_w=np.random.normal(size=keyboard.num_cumulants) * 0.1, ) _, ema_returns = experiment.run(env, agent, num_episodes=FLAGS.num_episodes, report_every=FLAGS.report_every, num_eval_reps=100) if FLAGS.output_path: experiment.write_returns_to_file(FLAGS.output_path, ema_returns)
def main(argv): del argv # Load the keyboard. keyboard = smart_module.SmartModuleImport(hub.Module(FLAGS.keyboard_path)) # Create the task environment. base_env_config = configs.get_fig4_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.EnvironmentWithKeyboardDirect( env=base_env, keyboard=keyboard, keyboard_ckpt_path=None, additional_discount=additional_discount, call_and_return=False) # Create the player agent. agent = regressed_agent.Agent( batch_size=10, optimizer_name="AdamOptimizer", # Disable training. optimizer_kwargs=dict(learning_rate=0.0,), init_w=[1., -1.]) returns = [] for _ in range(FLAGS.num_episodes): returns.append(experiment.run_episode(env, agent)) tf.logging.info("#" * 80) tf.logging.info( f"Avg. return over {FLAGS.num_episodes} episodes is {np.mean(returns)}") tf.logging.info("#" * 80)