def testSampleDoesNotCrossHead(self):
np.random.seed(12345)
data_spec = array_spec.ArraySpec((), np.int32)
replay_buffer = py_uniform_replay_buffer.PyUniformReplayBuffer(
data_spec=data_spec, capacity=10)
# Seed RB with 5 elements to move head to position 5.
for _ in range(5):
replay_buffer.add_batch(np.array([0]))
# Fill RB with elements 0-9.
for i in range(10):
replay_buffer.add_batch(np.array([i]))
# Sample with num_steps = 2. We should never sample (9, 0) since this is an
# invalid transition. With 1000 samples, the probability of sampling (9, 0)
# if it were not protected against would be (1 - (9/10)^10000) ~= 1.
sample_frequency = [0 for _ in range(10)]
for _ in range(10000):
(first, second) = replay_buffer.get_next(num_steps=2,
time_stacked=False)
self.assertNotEqual(np.array(9), first)
self.assertNotEqual(np.array(0), second)
sample_frequency[first] += 1
# 0-9 should all have been sampled about 10000/9 ~= 1111. We allow a delta
# of 150 off of 1111 -- the chance each sample frequency is within this
# range is 99.9998% (computed using the pmf of the binomial distribution).
# And since we fix the random seed, this test is repeatable.
for i in range(9):
self.assertAlmostEqual(10000 / 9, sample_frequency[i], delta=150)
def __init__(self, playerIndex, debug=False, create_model=True):
""" Initialize an agent. """
super().__init__(playerIndex, debug=debug)
self.trainable = True
# Whether to use small numbers for debugging reasons
self.use_small_numbers = use_small_nums
# Hyperparameters
self.alpha = 0.01 # learning rate
self.gamma = 0.95 # favour future rewards
self.exploration_decay_rate = 1 / 2000
self.reward_win_round = 0.005
self.reward_per_card_played = 0.001
self.rewards = {
0: 1.0, # No other agent finished before
1: 0.05, # One other agent finished before
2: 0.04, # Two other agents finished before
3: -1.0, # Three other agents finished before
}
# Training/Batch parameters
self.sample_batch = 64 if self.use_small_numbers else 512
self.replay_capacity = 128 if self.use_small_numbers else 1024
self.train_each_n_steps = 5 if self.use_small_numbers else 50
self.step_iteration = 0
self.model_data_spec = ( # TODO adjust to new model
tf.TensorSpec([4 * 13], tf.int8, "board_state"),
tf.TensorSpec([1], tf.float32, "q_value"),
)
self.replay_buffer = py_uniform_replay_buffer.PyUniformReplayBuffer(
capacity=self.replay_capacity,
data_spec=tensor_spec.to_nest_array_spec(self.model_data_spec)
)
# Validation parameters
self.val_replay_capacity = 20 if self.use_small_numbers else 200
self.validation_buffer = py_uniform_replay_buffer.PyUniformReplayBuffer(
capacity=self.val_replay_capacity,
data_spec=tensor_spec.to_nest_array_spec(self.model_data_spec)
)
# Initialize model
if create_model:
self._create_model()
def train_eval(
root_dir,
env_name='CartPole-v0',
num_iterations=100000,
fc_layer_params=(100, ),
# Params for collect
initial_collect_steps=1000,
collect_steps_per_iteration=1,
epsilon_greedy=0.1,
replay_buffer_capacity=100000,
# Params for target update
target_update_tau=0.05,
target_update_period=5,
# Params for train
train_steps_per_iteration=1,
batch_size=64,
learning_rate=1e-3,
n_step_update=1,
gamma=0.99,
reward_scale_factor=1.0,
gradient_clipping=None,
# Params for eval
num_eval_episodes=10,
eval_interval=1000,
# Params for checkpoints, summaries and logging
train_checkpoint_interval=10000,
policy_checkpoint_interval=5000,
log_interval=1000,
summaries_flush_secs=10,
debug_summaries=False,
summarize_grads_and_vars=False,
eval_metrics_callback=None):
"""A simple train and eval for DQN."""
root_dir = os.path.expanduser(root_dir)
train_dir = os.path.join(root_dir, 'train')
eval_dir = os.path.join(root_dir, 'eval')
train_summary_writer = tf.compat.v2.summary.create_file_writer(
train_dir, flush_millis=summaries_flush_secs * 1000)
train_summary_writer.set_as_default()
eval_summary_writer = tf.compat.v2.summary.create_file_writer(
eval_dir, flush_millis=summaries_flush_secs * 1000)
eval_metrics = [
py_metrics.AverageReturnMetric(buffer_size=num_eval_episodes),
py_metrics.AverageEpisodeLengthMetric(buffer_size=num_eval_episodes),
]
# Note this is a python environment.
env = batched_py_environment.BatchedPyEnvironment(
[suite_gym.load(env_name)])
eval_py_env = suite_gym.load(env_name)
# Convert specs to BoundedTensorSpec.
action_spec = tensor_spec.from_spec(env.action_spec())
observation_spec = tensor_spec.from_spec(env.observation_spec())
time_step_spec = ts.time_step_spec(observation_spec)
q_net = q_network.QNetwork(tensor_spec.from_spec(env.observation_spec()),
tensor_spec.from_spec(env.action_spec()),
fc_layer_params=fc_layer_params)
# The agent must be in graph.
global_step = tf.compat.v1.train.get_or_create_global_step()
agent = dqn_agent.DqnAgent(
time_step_spec,
action_spec,
q_network=q_net,
epsilon_greedy=epsilon_greedy,
n_step_update=n_step_update,
target_update_tau=target_update_tau,
target_update_period=target_update_period,
optimizer=tf.compat.v1.train.AdamOptimizer(
learning_rate=learning_rate),
td_errors_loss_fn=dqn_agent.element_wise_squared_loss,
gamma=gamma,
reward_scale_factor=reward_scale_factor,
gradient_clipping=gradient_clipping,
debug_summaries=debug_summaries,
summarize_grads_and_vars=summarize_grads_and_vars,
train_step_counter=global_step)
tf_collect_policy = agent.collect_policy
collect_policy = py_tf_policy.PyTFPolicy(tf_collect_policy)
greedy_policy = py_tf_policy.PyTFPolicy(agent.policy)
random_policy = random_py_policy.RandomPyPolicy(env.time_step_spec(),
env.action_spec())
# Python replay buffer.
replay_buffer = py_uniform_replay_buffer.PyUniformReplayBuffer(
capacity=replay_buffer_capacity,
data_spec=tensor_spec.to_nest_array_spec(agent.collect_data_spec))
time_step = env.reset()
# Initialize the replay buffer with some transitions. We use the random
# policy to initialize the replay buffer to make sure we get a good
# distribution of actions.
for _ in range(initial_collect_steps):
time_step = collect_step(env, time_step, random_policy, replay_buffer)
# TODO(b/112041045) Use global_step as counter.
train_checkpointer = common.Checkpointer(ckpt_dir=train_dir,
agent=agent,
global_step=global_step)
policy_checkpointer = common.Checkpointer(ckpt_dir=os.path.join(
train_dir, 'policy'),
policy=agent.policy,
global_step=global_step)
ds = replay_buffer.as_dataset(sample_batch_size=batch_size,
num_steps=n_step_update + 1)
ds = ds.prefetch(4)
itr = tf.compat.v1.data.make_initializable_iterator(ds)
experience = itr.get_next()
train_op = common.function(agent.train)(experience)
with eval_summary_writer.as_default(), \
tf.compat.v2.summary.record_if(True):
for eval_metric in eval_metrics:
eval_metric.tf_summaries(train_step=global_step)
with tf.compat.v1.Session() as session:
train_checkpointer.initialize_or_restore(session)
common.initialize_uninitialized_variables(session)
session.run(itr.initializer)
# Copy critic network values to the target critic network.
session.run(agent.initialize())
train = session.make_callable(train_op)
global_step_call = session.make_callable(global_step)
session.run(train_summary_writer.init())
session.run(eval_summary_writer.init())
# Compute initial evaluation metrics.
global_step_val = global_step_call()
metric_utils.compute_summaries(
eval_metrics,
eval_py_env,
greedy_policy,
num_episodes=num_eval_episodes,
global_step=global_step_val,
log=True,
callback=eval_metrics_callback,
)
timed_at_step = global_step_val
collect_time = 0
train_time = 0
steps_per_second_ph = tf.compat.v1.placeholder(tf.float32,
shape=(),
name='steps_per_sec_ph')
steps_per_second_summary = tf.compat.v2.summary.scalar(
name='global_steps_per_sec',
data=steps_per_second_ph,
step=global_step)
for _ in range(num_iterations):
start_time = time.time()
for _ in range(collect_steps_per_iteration):
time_step = collect_step(env, time_step, collect_policy,
replay_buffer)
collect_time += time.time() - start_time
start_time = time.time()
for _ in range(train_steps_per_iteration):
loss = train()
train_time += time.time() - start_time
global_step_val = global_step_call()
if global_step_val % log_interval == 0:
logging.info('step = %d, loss = %f', global_step_val,
loss.loss)
steps_per_sec = ((global_step_val - timed_at_step) /
(collect_time + train_time))
session.run(steps_per_second_summary,
feed_dict={steps_per_second_ph: steps_per_sec})
logging.info('%.3f steps/sec', steps_per_sec)
logging.info(
'%s', 'collect_time = {}, train_time = {}'.format(
collect_time, train_time))
timed_at_step = global_step_val
collect_time = 0
train_time = 0
if global_step_val % train_checkpoint_interval == 0:
train_checkpointer.save(global_step=global_step_val)
if global_step_val % policy_checkpoint_interval == 0:
policy_checkpointer.save(global_step=global_step_val)
if global_step_val % eval_interval == 0:
metric_utils.compute_summaries(
eval_metrics,
eval_py_env,
greedy_policy,
num_episodes=num_eval_episodes,
global_step=global_step_val,
log=True,
callback=eval_metrics_callback,
)
# Reset timing to avoid counting eval time.
timed_at_step = global_step_val
start_time = time.time()
