def test_ppo(self):
env_class = PolicyUnittestEnv
learning_rate = 1e-1
iterations = 20
batch_size = 100
steps_per_episode = 13
env = env_class(batch_size, steps_per_episode)
env = TFPyEnvironment(env)
eval_env = env_class(batch_size, steps_per_episode)
eval_env = TFPyEnvironment(eval_env)
algorithm = create_algorithm(env, learning_rate=learning_rate)
driver = SyncOffPolicyDriver(env,
algorithm,
debug_summaries=DEBUGGING,
summarize_grads_and_vars=DEBUGGING)
replayer = driver.exp_replayer
eval_driver = OnPolicyDriver(eval_env,
algorithm,
training=False,
greedy_predict=True)
env.reset()
eval_env.reset()
time_step = driver.get_initial_time_step()
policy_state = driver.get_initial_policy_state()
for i in range(iterations):
time_step, policy_state = driver.run(max_num_steps=batch_size *
steps_per_episode,
time_step=time_step,
policy_state=policy_state)
experience = replayer.replay_all()
driver.train(experience, num_updates=4, mini_batch_size=25)
replayer.clear()
eval_env.reset()
eval_time_step, _ = eval_driver.run(
max_num_steps=(steps_per_episode - 1) * batch_size)
logging.info("%d reward=%f", i,
float(tf.reduce_mean(eval_time_step.reward)))
eval_env.reset()
eval_time_step, _ = eval_driver.run(
max_num_steps=(steps_per_episode - 1) * batch_size)
logging.info("reward=%f", float(tf.reduce_mean(eval_time_step.reward)))
self.assertAlmostEqual(1.0,
float(tf.reduce_mean(eval_time_step.reward)),
delta=1e-1)
def test_off_policy_algorithm(self, algorithm_ctor, use_rollout_state,
sync_driver):
logging.info("{} {}".format(algorithm_ctor.__name__, sync_driver))
batch_size = 128
if use_rollout_state:
steps_per_episode = 5
mini_batch_length = 8
unroll_length = 8
env_class = RNNPolicyUnittestEnv
else:
steps_per_episode = 12
mini_batch_length = 2
unroll_length = 12
env_class = PolicyUnittestEnv
env = TFPyEnvironment(
env_class(batch_size,
steps_per_episode,
action_type=ActionType.Continuous))
eval_env = TFPyEnvironment(
env_class(batch_size,
steps_per_episode,
action_type=ActionType.Continuous))
algorithm = algorithm_ctor(env)
algorithm.use_rollout_state = use_rollout_state
if sync_driver:
driver = SyncOffPolicyDriver(env,
algorithm,
use_rollout_state=use_rollout_state,
debug_summaries=True,
summarize_grads_and_vars=True)
else:
driver = AsyncOffPolicyDriver(
[env],
algorithm,
use_rollout_state=algorithm.use_rollout_state,
num_actor_queues=1,
unroll_length=unroll_length,
learn_queue_cap=1,
actor_queue_cap=1,
debug_summaries=True,
summarize_grads_and_vars=True)
replayer = driver.exp_replayer
eval_driver = OnPolicyDriver(eval_env,
algorithm,
training=False,
greedy_predict=True)
eval_env.reset()
driver.start()
if sync_driver:
time_step = driver.get_initial_time_step()
policy_state = driver.get_initial_policy_state()
for i in range(5):
time_step, policy_state = driver.run(max_num_steps=batch_size *
steps_per_episode,
time_step=time_step,
policy_state=policy_state)
for i in range(500):
if sync_driver:
time_step, policy_state = driver.run(max_num_steps=batch_size *
mini_batch_length * 2,
time_step=time_step,
policy_state=policy_state)
experience, _ = replayer.replay(
sample_batch_size=128, mini_batch_length=mini_batch_length)
else:
driver.run_async()
experience = replayer.replay_all()
driver.train(experience,
mini_batch_size=128,
mini_batch_length=mini_batch_length)
eval_env.reset()
eval_time_step, _ = eval_driver.run(
max_num_steps=(steps_per_episode - 1) * batch_size)
logging.info("%d reward=%f", i,
float(tf.reduce_mean(eval_time_step.reward)))
driver.stop()
self.assertAlmostEqual(1.0,
float(tf.reduce_mean(eval_time_step.reward)),
delta=2e-1)
