def step(self):
with self.update_weights_timer:
if self.workers.remote_workers():
weights = ray.put(self.workers.local_worker().get_weights())
for e in self.workers.remote_workers():
e.set_weights.remote(weights)
with self.sample_timer:
if self.workers.remote_workers():
batches = ray_get_and_free(
[e.sample.remote() for e in self.workers.remote_workers()])
else:
batches = [self.workers.local_worker().sample()]
# Handle everything as if multiagent
tmp = []
for batch in batches:
if isinstance(batch, SampleBatch):
batch = MultiAgentBatch({DEFAULT_POLICY_ID: batch},
batch.count)
tmp.append(batch)
batches = tmp
for batch in batches:
if batch.count > self.max_buffer_size:
raise ValueError(
"The size of a single sample batch exceeds the replay "
"buffer size ({} > {})".format(batch.count,
self.max_buffer_size))
self.replay_buffer.append(batch)
self.num_steps_sampled += batch.count
self.buffer_size += batch.count
while self.buffer_size > self.max_buffer_size:
evicted = self.replay_buffer.pop(0)
self.buffer_size -= evicted.count
if self.num_steps_sampled >= self.replay_starts and self.num_steps_sampled % self.train_every == 0:
iter_extra_fetches = defaultdict(list)
with self.grad_timer:
for i in range(self.num_sgd_iter):
batch_fetches = self._sgd_step()
for k, v in batch_fetches.items():
iter_extra_fetches[k].append(v)
self.grad_timer.push_units_processed(self.train_batch_size *
self.num_sgd_iter)
return averaged(iter_extra_fetches)
else:
self.grad_timer = TimerStat()
self.learner_stats = {}
return {}
def __call__(self,
samples: SampleBatchType) -> (SampleBatchType, List[dict]):
_check_sample_batch_type(samples)
# Handle everything as if multiagent
if isinstance(samples, SampleBatch):
samples = MultiAgentBatch({
DEFAULT_POLICY_ID: samples
}, samples.count)
metrics = _get_shared_metrics()
load_timer = metrics.timers[LOAD_BATCH_TIMER]
learn_timer = metrics.timers[LEARN_ON_BATCH_TIMER]
with load_timer:
# (1) Load data into GPUs.
num_loaded_tuples = {}
for policy_id, batch in samples.policy_batches.items():
if policy_id not in self.policies:
continue
policy = self.workers.local_worker().get_policy(policy_id)
policy._debug_vars()
tuples = policy._get_loss_inputs_dict(
batch, shuffle=self.shuffle_sequences)
data_keys = list(policy._loss_input_dict_no_rnn.values())
if policy._state_inputs:
state_keys = policy._state_inputs + [policy._seq_lens]
else:
state_keys = []
num_loaded_tuples[policy_id] = (
self.optimizers[policy_id].load_data(
self.sess, [tuples[k] for k in data_keys],
[tuples[k] for k in state_keys]))
with learn_timer:
# (2) Execute minibatch SGD on loaded data.
fetches = {}
for policy_id, tuples_per_device in num_loaded_tuples.items():
optimizer = self.optimizers[policy_id]
num_batches = max(
1,
int(tuples_per_device) // int(self.per_device_batch_size))
logger.debug("== sgd epochs for {} ==".format(policy_id))
for i in range(self.num_sgd_iter):
iter_extra_fetches = defaultdict(list)
permutation = np.random.permutation(num_batches)
for batch_index in range(num_batches):
batch_fetches = optimizer.optimize(
self.sess, permutation[batch_index] *
self.per_device_batch_size)
for k, v in batch_fetches[LEARNER_STATS_KEY].items():
iter_extra_fetches[k].append(v)
if logger.getEffectiveLevel() <= logging.DEBUG:
avg = averaged(iter_extra_fetches)
logger.debug("{} {}".format(i, avg))
fetches[policy_id] = averaged(iter_extra_fetches, axis=0)
load_timer.push_units_processed(samples.count)
learn_timer.push_units_processed(samples.count)
metrics.counters[STEPS_TRAINED_COUNTER] += samples.count
metrics.info[LEARNER_INFO] = fetches
if self.workers.remote_workers():
with metrics.timers[WORKER_UPDATE_TIMER]:
weights = ray.put(self.workers.local_worker().get_weights(
self.policies))
for e in self.workers.remote_workers():
e.set_weights.remote(weights, _get_global_vars())
# Also update global vars of the local worker.
self.workers.local_worker().set_global_vars(_get_global_vars())
return samples, fetches
def step(self):
with self.update_weights_timer:
if self.workers.remote_workers():
weights = ray.put(self.workers.local_worker().get_weights())
for e in self.workers.remote_workers():
e.set_weights.remote(weights)
with self.sample_timer:
if self.workers.remote_workers():
samples = collect_samples(self.workers.remote_workers(),
self.rollout_fragment_length,
self.num_envs_per_worker,
self.train_batch_size)
if samples.count > self.train_batch_size * 2:
logger.info(
"Collected more training samples than expected "
"(actual={}, train_batch_size={}). ".format(
samples.count, self.train_batch_size) +
"This may be because you have many workers or "
"long episodes in 'complete_episodes' batch mode.")
else:
samples = []
while sum(s.count for s in samples) < self.train_batch_size:
samples.append(self.workers.local_worker().sample())
samples = SampleBatch.concat_samples(samples)
# Handle everything as if multiagent
if isinstance(samples, SampleBatch):
samples = MultiAgentBatch({
DEFAULT_POLICY_ID: samples
}, samples.count)
for policy_id, policy in self.policies.items():
if policy_id not in samples.policy_batches:
continue
batch = samples.policy_batches[policy_id]
for field in self.standardize_fields:
value = batch[field]
standardized = (value - value.mean()) / max(1e-4, value.std())
batch[field] = standardized
num_loaded_tuples = {}
with self.load_timer:
for policy_id, batch in samples.policy_batches.items():
if policy_id not in self.policies:
continue
policy = self.policies[policy_id]
policy._debug_vars()
tuples = policy._get_loss_inputs_dict(
batch, shuffle=self.shuffle_sequences)
data_keys = [ph for _, ph in policy._loss_inputs]
if policy._state_inputs:
state_keys = policy._state_inputs + [policy._seq_lens]
else:
state_keys = []
num_loaded_tuples[policy_id] = (
self.optimizers[policy_id].load_data(
self.sess, [tuples[k] for k in data_keys],
[tuples[k] for k in state_keys]))
fetches = {}
with self.grad_timer:
for policy_id, tuples_per_device in num_loaded_tuples.items():
optimizer = self.optimizers[policy_id]
num_batches = max(
1,
int(tuples_per_device) // int(self.per_device_batch_size))
logger.debug("== sgd epochs for {} ==".format(policy_id))
for i in range(self.num_sgd_iter):
iter_extra_fetches = defaultdict(list)
permutation = np.random.permutation(num_batches)
for batch_index in range(num_batches):
batch_fetches = optimizer.optimize(
self.sess, permutation[batch_index] *
self.per_device_batch_size)
for k, v in batch_fetches[LEARNER_STATS_KEY].items():
iter_extra_fetches[k].append(v)
logger.debug("{} {}".format(i,
averaged(iter_extra_fetches)))
fetches[policy_id] = averaged(iter_extra_fetches)
self.num_steps_sampled += samples.count
self.num_steps_trained += tuples_per_device * len(self.devices)
self.learner_stats = fetches
return fetches