def testEpsilonGreedyAction(self):
policy = epsilon_greedy_policy.EpsilonGreedyPolicy(
self._mock_policy, 0.9, 0.1, 0.01)
action = policy.action(np.arange(2))
self.assertAllClose([0.98203928, 0.3999047, 0.8441526], action)
action = policy.action(np.arange(2))
self.assertAllClose([0.54484307, 0.61947784, 0.33183679], action)
action = policy.action(np.arange(2))
self.assertAllClose([.5, .5, .5], action)
def testEpsilonDecay(self):
epsilon = 0.9
decay_rate = 0.7
epsilon_min = 0.01
policy = epsilon_greedy_policy.EpsilonGreedyPolicy(
self._mock_policy, epsilon, decay_rate, epsilon_min)
for _ in range(20):
policy.update_params()
epsilon = max(epsilon * decay_rate, epsilon_min)
self.assertAlmostEqual(epsilon, policy.epsilon)
def main(_):
logging.set_verbosity(logging.INFO)
assert FLAGS.replay_memory_capacity > FLAGS.batch_size * FLAGS.train_steps_per_iteration
replay_memory = replay_memory_lib.ReplayMemory(
name='ReplayBuffer', capacity=FLAGS.replay_memory_capacity)
replay_memory.restore(FLAGS.checkpoint_dir)
env = utils.create_env(FLAGS.env_name)
state_spec, action_spec = utils.get_state_and_action_specs(
env, action_bounds=FLAGS.action_bounds)
hidden_layers = [int(h) for h in FLAGS.hidden_layers]
summary_writer = None
if FLAGS.result_dir is not None:
hparam_dict = {
'env_name': FLAGS.env_name,
'discount_factor': FLAGS.discount_factor,
'time_out': FLAGS.time_out,
'action_bounds': FLAGS.action_bounds,
'max_iterations': FLAGS.max_iterations,
'num_episodes_per_iteration': FLAGS.num_episodes_per_iteration,
'collect_experience_parallelism':
FLAGS.collect_experience_parallelism,
'hidden_layers': FLAGS.hidden_layers,
'batch_size': FLAGS.batch_size,
'train_steps_per_iteration': FLAGS.train_steps_per_iteration,
'target_update_steps': FLAGS.target_update_steps,
'learning_rate': FLAGS.learning_rate,
'learning_rate_action': FLAGS.learning_rate_action,
'learning_rate_ga': FLAGS.learning_rate_ga,
'action_maximization_iterations':
FLAGS.action_maximization_iterations,
'tau_copy': FLAGS.tau_copy,
'clipped_target': FLAGS.clipped_target,
'hard_update_steps': FLAGS.hard_update_steps,
'l2_loss_flag': FLAGS.l2_loss_flag,
'simple_lambda_flag': FLAGS.simple_lambda_flag,
'dual_filter_clustering_flag': FLAGS.dual_filter_clustering_flag,
'solver': FLAGS.solver,
'initial_lambda': FLAGS.initial_lambda,
'tolerance_init': FLAGS.tolerance_init,
'tolerance_min': FLAGS.tolerance_min,
'tolerance_max': FLAGS.tolerance_max,
'tolerance_decay': FLAGS.tolerance_decay,
'warmstart': FLAGS.warmstart,
'dual_q_label': FLAGS.dual_q_label,
'seed': FLAGS.seed,
}
if FLAGS.exploration_policy == 'egreedy':
hparam_dict.update({
'epsilon': FLAGS.epsilon,
'epsilon_decay': FLAGS.epsilon_decay,
'epsilon_min': FLAGS.epsilon_min,
})
elif FLAGS.exploration_policy == 'gaussian':
hparam_dict.update({
'sigma': FLAGS.sigma,
'sigma_decay': FLAGS.sigma_decay,
'sigma_min': FLAGS.sigma_min,
})
utils.save_hparam_config(hparam_dict, FLAGS.result_dir)
summary_writer = tf.summary.FileWriter(FLAGS.result_dir)
with tf.Session() as sess:
agent = caql_agent.CaqlAgent(
session=sess,
state_spec=state_spec,
action_spec=action_spec,
discount_factor=FLAGS.discount_factor,
hidden_layers=hidden_layers,
learning_rate=FLAGS.learning_rate,
learning_rate_action=FLAGS.learning_rate_action,
learning_rate_ga=FLAGS.learning_rate_ga,
action_maximization_iterations=FLAGS.
action_maximization_iterations,
tau_copy=FLAGS.tau_copy,
clipped_target_flag=FLAGS.clipped_target,
hard_update_steps=FLAGS.hard_update_steps,
batch_size=FLAGS.batch_size,
l2_loss_flag=FLAGS.l2_loss_flag,
simple_lambda_flag=FLAGS.simple_lambda_flag,
dual_filter_clustering_flag=FLAGS.dual_filter_clustering_flag,
solver=FLAGS.solver,
dual_q_label=FLAGS.dual_q_label,
initial_lambda=FLAGS.initial_lambda,
tolerance_min_max=[FLAGS.tolerance_min, FLAGS.tolerance_max])
saver = tf.train.Saver(max_to_keep=None)
step = agent.initialize(saver, FLAGS.checkpoint_dir)
iteration = int(step / FLAGS.train_steps_per_iteration)
if iteration >= FLAGS.max_iterations:
return
greedy_policy = agent_policy.AgentPolicy(action_spec, agent)
if FLAGS.exploration_policy == 'egreedy':
epsilon_init = max(
FLAGS.epsilon * (FLAGS.epsilon_decay**iteration),
FLAGS.epsilon_min)
behavior_policy = epsilon_greedy_policy.EpsilonGreedyPolicy(
greedy_policy, epsilon_init, FLAGS.epsilon_decay,
FLAGS.epsilon_min)
elif FLAGS.exploration_policy == 'gaussian':
sigma_init = max(FLAGS.sigma * (FLAGS.sigma_decay**iteration),
FLAGS.sigma_min)
behavior_policy = gaussian_noise_policy.GaussianNoisePolicy(
greedy_policy, sigma_init, FLAGS.sigma_decay, FLAGS.sigma_min)
elif FLAGS.exploration_policy == 'none':
behavior_policy = greedy_policy
logging.info('Start with iteration %d, step %d, %s', iteration, step,
behavior_policy.params_debug_str())
while iteration < FLAGS.max_iterations:
utils.collect_experience_parallel(
num_episodes=FLAGS.num_episodes_per_iteration,
session=sess,
behavior_policy=behavior_policy,
time_out=FLAGS.time_out,
discount_factor=FLAGS.discount_factor,
replay_memory=replay_memory)
if (replay_memory.size <
FLAGS.batch_size * FLAGS.train_steps_per_iteration):
continue
tf_summary = None
if summary_writer:
tf_summary = tf.Summary()
q_function_losses = []
q_vals = []
lambda_function_losses = []
action_function_losses = []
portion_active_data = []
portion_active_data_and_clusters = []
ts_begin = time.time()
# 'step' can be started from any number if the program is restored from
# a checkpoint after crash or pre-emption.
local_step = step % FLAGS.train_steps_per_iteration
while local_step < FLAGS.train_steps_per_iteration:
minibatch = replay_memory.sample(FLAGS.batch_size)
if FLAGS.tolerance_decay is not None:
tolerance_decay = FLAGS.tolerance_decay**iteration
else:
tolerance_decay = None
# Leave summary only for the last one.
agent_tf_summary_vals = None
if local_step == FLAGS.train_steps_per_iteration - 1:
agent_tf_summary_vals = []
# train q_function and lambda_function networks
(q_function_loss, target_q_vals, lambda_function_loss,
best_train_label_batch, portion_active_constraint,
portion_active_constraint_and_cluster) = (
agent.train_q_function_network(minibatch,
FLAGS.tolerance_init,
tolerance_decay,
FLAGS.warmstart,
agent_tf_summary_vals))
action_function_loss = agent.train_action_function_network(
best_train_label_batch)
q_function_losses.append(q_function_loss)
q_vals.append(target_q_vals)
lambda_function_losses.append(lambda_function_loss)
action_function_losses.append(action_function_loss)
portion_active_data.append(portion_active_constraint)
portion_active_data_and_clusters.append(
portion_active_constraint_and_cluster)
local_step += 1
step += 1
if step % FLAGS.target_update_steps == 0:
agent.update_target_network()
if FLAGS.clipped_target and step % FLAGS.hard_update_steps == 0:
agent.update_target_network2()
elapsed_secs = time.time() - ts_begin
steps_per_sec = FLAGS.train_steps_per_iteration / elapsed_secs
iteration += 1
logging.info(
'Iteration: %d, steps per sec: %.2f, replay memory size: %d, %s, '
'avg q_function loss: %.3f, '
'avg lambda_function loss: %.3f, '
'avg action_function loss: %.3f '
'avg portion active data: %.3f '
'avg portion active data and cluster: %.3f ', iteration,
steps_per_sec, replay_memory.size,
behavior_policy.params_debug_str(), np.mean(q_function_losses),
np.mean(lambda_function_losses),
np.mean(action_function_losses), np.mean(portion_active_data),
np.mean(portion_active_data_and_clusters))
if tf_summary:
if agent_tf_summary_vals:
tf_summary.value.extend(agent_tf_summary_vals)
tf_summary.value.extend([
tf.Summary.Value(tag='steps_per_sec',
simple_value=steps_per_sec),
tf.Summary.Value(tag='avg_q_loss',
simple_value=np.mean(q_function_loss)),
tf.Summary.Value(tag='avg_q_val',
simple_value=np.mean(q_vals)),
tf.Summary.Value(
tag='avg_portion_active_data',
simple_value=np.mean(portion_active_data)),
tf.Summary.Value(
tag='avg_portion_active_data_and_cluster',
simple_value=np.mean(portion_active_data_and_clusters))
])
behavior_policy.update_params()
utils.periodic_updates(iteration=iteration,
train_step=step,
replay_memories=(replay_memory, ),
greedy_policy=greedy_policy,
use_action_function=True,
saver=saver,
sess=sess,
time_out=FLAGS.time_out,
tf_summary=tf_summary)
if summary_writer and tf_summary:
summary_writer.add_summary(tf_summary, step)
logging.info('Training is done.')
env.close()