def setUpClass(cls): cls.parser = apex_argument() cls.parser.set_defaults(n_training=10) cls.parser.set_defaults(param_update_freq=1) cls.parser.set_defaults(test_freq=10) cls.parser.set_defaults(n_explorer=2) cls.parser.set_defaults(n_env=4) cls.parser.set_defaults(local_buffer_size=64)
def test_run(parser): parser = apex_argument() parser.set_defaults(n_training=10) parser.set_defaults(param_update_freq=1) parser.set_defaults(test_freq=10) parser.set_defaults(n_env=64) parser.set_defaults(local_buffer_size=64) _test_run_discrete(parser) _test_run_continuous(parser)
critic_units=[400, 300], memory_capacity=memory_capacity) def get_weights_fn(policy): # TODO: Check if following needed import tensorflow as tf with tf.device(policy.device): return [ policy.actor.weights, policy.critic.weights, policy.critic_target.weights ] def set_weights_fn(policy, weights): actor_weights, critic_weights, critic_target_weights = weights update_target_variables(policy.actor.weights, actor_weights, tau=1.) update_target_variables(policy.critic.weights, critic_weights, tau=1.) update_target_variables(policy.critic_target.weights, critic_target_weights, tau=1.) if __name__ == '__main__': parser = apex_argument() parser.add_argument('--env-name', type=str, default="Pendulum-v0") parser = DDPG.get_argument(parser) args = parser.parse_args() run(args, env_fn(args.env_name), policy_fn, get_weights_fn, set_weights_fn)