def _build_replay_buffer(self, use_staging):
"""Creates the replay buffer used by the agent.
Args:
use_staging: bool, if True, uses a staging area to prefetch data for
faster training.
Returns:
`WrappedPrioritizedReplayBuffer` object.
Raises:
ValueError: if given an invalid replay scheme.
"""
print('in RGBGripper rainbow _build_replay_buffer')
if self._replay_scheme not in ['uniform', 'prioritized']:
raise ValueError('Invalid replay scheme: {}'.format(
self._replay_scheme))
return prioritized_replay_buffer.WrappedPrioritizedReplayBuffer(
observation_shape=dqn_agent.OBSERVATION_SHAPE,
stack_size=dqn_agent.STACK_SIZE,
use_staging=use_staging,
update_horizon=self.update_horizon,
gamma=self.gamma,
extra_storage_types=[ReplayElement(
'gripper', (),
np.uint8)]) # , ReplayElement('next_gripper', (), np.uint8)
'''
def _build_replay_buffer(self, use_staging):
"""Creates the replay buffer used by the agent.
Args:
use_staging: bool, if True, uses a staging area to prefetch data for
faster training.
Returns:
A `WrappedPrioritizedReplayBuffer` object.
Raises:
ValueError: if given an invalid replay scheme.
"""
if self._replay_scheme not in ['uniform', 'prioritized']:
raise ValueError('Invalid replay scheme: {}'.format(
self._replay_scheme))
# Both replay schemes use the same data structure, but the 'uniform' scheme
# sets all priorities to the same value (which yields uniform sampling).
return prioritized_replay_buffer.WrappedPrioritizedReplayBuffer(
observation_shape=self.observation_shape,
stack_size=self.stack_size,
use_staging=use_staging,
update_horizon=self.update_horizon,
gamma=self.gamma,
observation_dtype=self.observation_dtype.as_numpy_dtype)
def create_default_memory(self):
return prioritized_replay_buffer.WrappedPrioritizedReplayBuffer(
SCREEN_SIZE,
STACK_SIZE,
use_staging=False,
replay_capacity=REPLAY_CAPACITY,
batch_size=BATCH_SIZE,
max_sample_attempts=10) # For faster tests.
def _build_replay_buffer(self, use_staging):
if self._replay_scheme not in ['uniform', 'prioritized']:
raise ValueError('Invalid replay scheme: {}'.format(
self._replay_scheme))
return prioritized_replay_buffer.WrappedPrioritizedReplayBuffer(
observation_shape=self.observation_shape,
stack_size=self.stack_size,
use_staging=use_staging,
update_horizon=self.update_horizon,
gamma=self.gamma,
observation_dtype=self.observation_dtype.as_numpy_dtype)
def _build_replay_buffer(self, use_staging):
if self._replay_scheme not in ['uniform', 'prioritized']:
raise ValueError('Invalid replay scheme: {}'.format(self._replay_scheme))
# Both replay schemes use the same data structure, but the 'uniform' scheme
# sets all priorities to the same value (which yields uniform sampling).
return prioritized_replay_buffer.WrappedPrioritizedReplayBuffer(
observation_shape=self.observation_shape,
stack_size=self.stack_size,
use_staging=use_staging,
update_horizon=self.update_horizon,
gamma=self.gamma,
observation_dtype=self.observation_dtype.as_numpy_dtype)
def _build_replay_buffer(self, use_staging):
"""Creates the replay buffer used by the agent.
Args:
use_staging: bool, if True, uses a staging area to prefetch data for
faster training.
Returns:
A `WrappedPrioritizedReplayBuffer` object.
Raises:
ValueError: if given an invalid replay scheme.
"""
if self._replay_scheme not in ['uniform', 'prioritized']:
raise ValueError('Invalid replay scheme: {}'.format(self._replay_scheme))
return prioritized_replay_buffer.WrappedPrioritizedReplayBuffer(
observation_shape=dqn_agent.OBSERVATION_SHAPE,
stack_size=dqn_agent.STACK_SIZE,
use_staging=use_staging,
update_horizon=self.update_horizon,
gamma=self.gamma)
