class LegacyApexMemory(Specifiable):
"""
Apex prioritized replay implementing compression.
"""
def __init__(self,
capacity=1000,
alpha=1.0,
beta=1.0,
n_step_adjustment=1):
"""
TODO: documentation.
Args:
capacity ():
alpha ():
beta ():
n_step_adjustment ():
"""
super(LegacyApexMemory, self).__init__()
self.memory_values = []
self.index = 0
self.capacity = capacity
self.size = 0
self.max_priority = 1.0
self.alpha = alpha
self.beta = beta
assert n_step_adjustment > 0, "ERROR: n-step adjustment must be at least 1 where 1 corresponds" \
"to the direct next state."
self.n_step_adjustment = n_step_adjustment
self.default_new_weight = np.power(self.max_priority, self.alpha)
self.priority_capacity = 1
while self.priority_capacity < self.capacity:
self.priority_capacity *= 2
# Create segment trees, initialize with neutral elements.
sum_values = [0.0 for _ in range_(2 * self.priority_capacity)]
sum_segment_tree = MemSegmentTree(sum_values, self.priority_capacity,
operator.add)
min_values = [float('inf') for _ in range_(2 * self.priority_capacity)]
min_segment_tree = MemSegmentTree(min_values, self.priority_capacity,
min)
self.merged_segment_tree = MinSumSegmentTree(
sum_tree=sum_segment_tree,
min_tree=min_segment_tree,
capacity=self.priority_capacity)
def insert_records(self, record):
# TODO: This has the record interface, but actually expects a specific structure anyway, so
# may as well change API?
if self.index >= self.size:
self.memory_values.append(record)
else:
self.memory_values[self.index] = record
# Weights. # TODO this is problematic due to index not existing.
if record[4] is not None:
self.merged_segment_tree.insert(self.index, record[4])
else:
self.merged_segment_tree.insert(self.index,
self.default_new_weight)
# Update indices.
self.index = (self.index + 1) % self.capacity
self.size = min(self.size + 1, self.capacity)
def read_records(self, indices):
"""
Obtains record values for the provided indices.
Args:
indices ndarray: Indices to read. Assumed to be not contiguous.
Returns:
dict: Record value dict.
"""
states = list()
actions = list()
rewards = list()
terminals = list()
next_states = list()
for index in indices:
# TODO remove as array casts if they dont help.
state, action, reward, terminal, weight = self.memory_values[index]
decompressed_state = np.array(ray_decompress(state), copy=False)
states.append(decompressed_state)
actions.append(np.array(action, copy=False))
rewards.append(reward)
terminals.append(terminal)
decompressed_next_state = decompressed_state
# If terminal -> just use same state, already decompressed
if terminal:
next_states.append(decompressed_next_state)
else:
# Otherwise advance until correct next state or terminal.
next_state = decompressed_next_state
for i in range_(self.n_step_adjustment):
next_index = (index + i + 1) % self.size
next_state, _, _, terminal, _ = self.memory_values[
next_index]
if terminal:
break
next_states.append(
np.array(ray_decompress(next_state), copy=False))
return dict(states=np.array(states),
actions=np.array(actions),
rewards=np.array(rewards),
terminals=np.array(terminals),
next_states=np.array(next_states))
def get_records(self, num_records):
indices = []
# Ensure we always have n-next states.
# TODO potentially block this if size - 1 - nstep < 1?
prob_sum = self.merged_segment_tree.sum_segment_tree.get_sum(
0, self.size - 1 - self.n_step_adjustment)
samples = np.random.random(size=(num_records, )) * prob_sum
for sample in samples:
indices.append(
self.merged_segment_tree.sum_segment_tree.index_of_prefixsum(
prefix_sum=sample))
sum_prob = self.merged_segment_tree.sum_segment_tree.get_sum()
min_prob = self.merged_segment_tree.min_segment_tree.get_min_value(
) / sum_prob
max_weight = (min_prob * self.size)**(-self.beta)
weights = []
for index in indices:
sample_prob = self.merged_segment_tree.sum_segment_tree.get(
index) / sum_prob
weight = (sample_prob * self.size)**(-self.beta)
weights.append(weight / max_weight)
indices = np.array(indices, copy=False)
return self.read_records(indices=indices), indices, np.array(
weights, copy=False)
def update_records(self, indices, update):
for index, loss in zip(indices, update):
priority = np.power(loss, self.alpha)
self.merged_segment_tree.insert(index, priority)
self.max_priority = max(self.max_priority, priority)
class ApexMemory(Specifiable):
"""
Apex prioritized replay implementing compression.
"""
def __init__(self,
capacity=1000,
alpha=1.0,
beta=1.0,
n_step_adjustment=1):
"""
TODO: documentation.
Args:
capacity ():
alpha ():
beta ():
n_step_adjustment ():
"""
super(ApexMemory, self).__init__()
self.memory_values = []
self.index = 0
self.capacity = capacity
self.size = 0
self.max_priority = 1.0
self.alpha = alpha
self.beta = beta
# TODO this is not used here any more
# assert n_step_adjustment > 0, "ERROR: n-step adjustment must be at least 1 where 1 corresponds" \
# "to the direct next state."
self.n_step_adjustment = n_step_adjustment
self.default_new_weight = np.power(self.max_priority, self.alpha)
self.priority_capacity = 1
while self.priority_capacity < self.capacity:
self.priority_capacity *= 2
# Create segment trees, initialize with neutral elements.
sum_values = [0.0 for _ in range_(2 * self.priority_capacity)]
sum_segment_tree = MemSegmentTree(sum_values, self.priority_capacity,
operator.add)
min_values = [float('inf') for _ in range_(2 * self.priority_capacity)]
min_segment_tree = MemSegmentTree(min_values, self.priority_capacity,
min)
self.merged_segment_tree = MinSumSegmentTree(
sum_tree=sum_segment_tree,
min_tree=min_segment_tree,
capacity=self.priority_capacity)
def insert_records(self, record):
# TODO: This has the record interface, but actually expects a specific structure anyway, so
# may as well change API?
if self.index >= self.size:
self.memory_values.append(record)
else:
self.memory_values[self.index] = record
# Weights. # TODO this is problematic due to index not existing.
if record[5] is not None:
self.merged_segment_tree.insert(self.index, record[5]**self.alpha)
else:
self.merged_segment_tree.insert(self.index,
self.max_priority**self.alpha)
# Update indices.
self.index = (self.index + 1) % self.capacity
self.size = min(self.size + 1, self.capacity)
def read_records(self, indices):
"""
Obtains record values for the provided indices.
Args:
indices ndarray: Indices to read. Assumed to be not contiguous.
Returns:
dict: Record value dict.
"""
states = []
actions = []
rewards = []
terminals = []
next_states = []
for index in indices:
state, action, reward, terminal, next_state, weight = self.memory_values[
index]
states.append(ray_decompress(state))
actions.append(action)
rewards.append(reward)
terminals.append(terminal)
next_states.append(ray_decompress(next_state))
return dict(states=np.asarray(states),
actions=np.asarray(actions),
rewards=np.asarray(rewards),
terminals=np.asarray(terminals),
next_states=np.asarray(next_states))
def get_records(self, num_records):
indices = []
prob_sum = self.merged_segment_tree.sum_segment_tree.get_sum(
0, self.size)
samples = np.random.random(size=(num_records, )) * prob_sum
for sample in samples:
indices.append(
self.merged_segment_tree.sum_segment_tree.index_of_prefixsum(
prefix_sum=sample))
sum_prob = self.merged_segment_tree.sum_segment_tree.get_sum()
min_prob = self.merged_segment_tree.min_segment_tree.get_min_value(
) / sum_prob + SMALL_NUMBER
max_weight = (min_prob * self.size)**(-self.beta)
weights = []
for index in indices:
sample_prob = self.merged_segment_tree.sum_segment_tree.get(
index) / sum_prob
weight = (sample_prob * self.size)**(-self.beta)
weights.append(weight / max_weight)
return self.read_records(
indices=indices), np.asarray(indices), np.asarray(weights)
def update_records(self, indices, update):
for index, loss in zip(indices, update):
self.merged_segment_tree.insert(index, loss**self.alpha)
self.max_priority = max(self.max_priority, loss)
class MemPrioritizedReplay(Memory):
"""
Implements an in-memory prioritized replay.
API:
update_records(indices, update) -> Updates the given indices with the given priority scores.
"""
def __init__(self, capacity=1000, next_states=True, alpha=1.0, beta=0.0):
super(MemPrioritizedReplay, self).__init__()
self.memory_values = []
self.index = 0
self.capacity = capacity
self.size = 0
self.max_priority = 1.0
self.alpha = alpha
self.beta = beta
self.next_states = next_states
self.default_new_weight = np.power(self.max_priority, self.alpha)
def create_variables(self, input_spaces, action_space=None):
# Store our record-space for convenience.
self.record_space = input_spaces["records"]
self.record_space_flat = Dict(self.record_space.flatten(
custom_scope_separator="/", scope_separator_at_start=False),
add_batch_rank=True)
self.priority_capacity = 1
while self.priority_capacity < self.capacity:
self.priority_capacity *= 2
# Create segment trees, initialize with neutral elements.
sum_values = [0.0 for _ in range_(2 * self.priority_capacity)]
sum_segment_tree = MemSegmentTree(sum_values, self.priority_capacity,
operator.add)
min_values = [float('inf') for _ in range_(2 * self.priority_capacity)]
min_segment_tree = MemSegmentTree(min_values, self.priority_capacity,
min)
self.merged_segment_tree = MinSumSegmentTree(
sum_tree=sum_segment_tree,
min_tree=min_segment_tree,
capacity=self.priority_capacity)
def _read_records(self, indices):
"""
Obtains record values for the provided indices.
Args:
indices ndarray: Indices to read. Assumed to be not contiguous.
Returns:
dict: Record value dict.
"""
records = {}
for name in self.record_space_flat.keys():
records[name] = []
if self.size > 0:
for index in indices:
record = self.memory_values[index]
for name in self.record_space_flat.keys():
records[name].append(record[name])
else:
# TODO figure out how to do default handling in pytorch builds.
# Fill with default vals for build.
for name in self.record_space_flat.keys():
if get_backend() == "pytorch":
records[name] = torch.zeros(
self.record_space_flat[name].shape,
dtype=dtype_(self.record_space_flat[name].dtype,
"pytorch"))
else:
records[name] = np.zeros(
self.record_space_flat[name].shape)
# Convert if necessary: list of tensors fails at space inference otherwise.
if get_backend() == "pytorch":
for name in self.record_space_flat.keys():
records[name] = torch.squeeze(torch.stack(records[name]))
return records
@rlgraph_api(flatten_ops=True)
def _graph_fn_insert_records(self, records):
if records is None or get_rank(records['rewards']) == 0:
return
num_records = len(records['rewards'])
if num_records == 1:
if self.index >= self.size:
self.memory_values.append(records)
else:
self.memory_values[self.index] = records
self.merged_segment_tree.insert(self.index,
self.default_new_weight)
else:
insert_indices = np.arange(
start=self.index,
stop=self.index + num_records) % self.capacity
i = 0
for insert_index in insert_indices:
self.merged_segment_tree.insert(insert_index,
self.default_new_weight)
record = dict()
for name, record_values in records.items():
record[name] = record_values[i]
if insert_index >= self.size:
self.memory_values.append(record)
else:
self.memory_values[insert_index] = record
i += 1
# Update indices
self.index = (self.index + num_records) % self.capacity
self.size = min(self.size + num_records, self.capacity)
@rlgraph_api
def _graph_fn_get_records(self, num_records=1):
indices = []
prob_sum = self.merged_segment_tree.sum_segment_tree.get_sum(
0, self.size - 1)
samples = np.random.random(size=(num_records, )) * prob_sum
for sample in samples:
indices.append(
self.merged_segment_tree.sum_segment_tree.index_of_prefixsum(
prefix_sum=sample))
sum_prob = self.merged_segment_tree.sum_segment_tree.get_sum(
) + SMALL_NUMBER
min_prob = self.merged_segment_tree.min_segment_tree.get_min_value(
) / sum_prob
max_weight = (min_prob * self.size)**(-self.beta)
weights = []
for index in indices:
sample_prob = self.merged_segment_tree.sum_segment_tree.get(
index) / sum_prob
weight = (sample_prob * self.size)**(-self.beta)
weights.append(weight / max_weight)
if get_backend() == "pytorch":
indices = torch.tensor(indices)
weights = torch.tensor(weights)
else:
indices = np.asarray(indices)
weights = np.asarray(weights)
return self._read_records(indices=indices), indices, weights
@rlgraph_api(must_be_complete=False)
def _graph_fn_update_records(self, indices, update):
if len(indices) > 0 and indices[0]:
for index, loss in zip(indices, update):
priority = np.power(loss, self.alpha)
self.merged_segment_tree.insert(index, priority)
self.max_priority = max(self.max_priority, priority)
class MemPrioritizedReplay(Memory):
"""
Implements an in-memory prioritized replay.
API:
update_records(indices, update) -> Updates the given indices with the given priority scores.
"""
def __init__(self, capacity=1000, next_states=True, alpha=1.0, beta=0.0):
super(MemPrioritizedReplay, self).__init__()
self.memory_values = []
self.index = 0
self.capacity = capacity
self.size = 0
self.max_priority = 1.0
self.alpha = alpha
self.beta = beta
self.next_states = next_states
self.default_new_weight = np.power(self.max_priority, self.alpha)
def create_variables(self, input_spaces, action_space=None):
super(MemPrioritizedReplay,
self).create_variables(input_spaces, action_space)
self.priority_capacity = 1
while self.priority_capacity < self.capacity:
self.priority_capacity *= 2
# Create segment trees, initialize with neutral elements.
sum_values = [0.0 for _ in range_(2 * self.priority_capacity)]
sum_segment_tree = MemSegmentTree(sum_values, self.priority_capacity,
operator.add)
min_values = [float('inf') for _ in range_(2 * self.priority_capacity)]
min_segment_tree = MemSegmentTree(min_values, self.priority_capacity,
min)
self.merged_segment_tree = MinSumSegmentTree(
sum_tree=sum_segment_tree,
min_tree=min_segment_tree,
capacity=self.priority_capacity)
@rlgraph_api(flatten_ops=True)
def _graph_fn_insert_records(self, records):
if records is None or get_rank(records[self.terminal_key]) == 0:
return
num_records = len(records[self.terminal_key])
if num_records == 1:
if self.index >= self.size:
self.memory_values.append(records)
else:
self.memory_values[self.index] = records
self.merged_segment_tree.insert(self.index,
self.default_new_weight)
else:
insert_indices = np.arange(
start=self.index,
stop=self.index + num_records) % self.capacity
i = 0
for insert_index in insert_indices:
self.merged_segment_tree.insert(insert_index,
self.default_new_weight)
record = {}
for name, record_values in records.items():
record[name] = record_values[i]
if insert_index >= self.size:
self.memory_values.append(record)
else:
self.memory_values[insert_index] = record
i += 1
# Update indices
self.index = (self.index + num_records) % self.capacity
self.size = min(self.size + num_records, self.capacity)
@rlgraph_api
def _graph_fn_get_records(self, num_records=1):
available_records = min(num_records, self.size)
indices = []
prob_sum = self.merged_segment_tree.sum_segment_tree.get_sum(
0, self.size - 1)
samples = np.random.random(size=(available_records, )) * prob_sum
for sample in samples:
indices.append(
self.merged_segment_tree.sum_segment_tree.index_of_prefixsum(
prefix_sum=sample))
sum_prob = self.merged_segment_tree.sum_segment_tree.get_sum(
) + SMALL_NUMBER
min_prob = self.merged_segment_tree.min_segment_tree.get_min_value(
) / sum_prob
max_weight = (min_prob * self.size)**(-self.beta)
weights = []
for index in indices:
sample_prob = self.merged_segment_tree.sum_segment_tree.get(
index) / sum_prob
weight = (sample_prob * self.size)**(-self.beta)
weights.append(weight / max_weight)
if get_backend() == "pytorch":
indices = torch.tensor(indices)
weights = torch.tensor(weights)
else:
indices = np.asarray(indices)
weights = np.asarray(weights)
records = DataOpDict()
for name, variable in self.memory.items():
records[name] = self.read_variable(
variable,
indices,
dtype=util.convert_dtype(self.flat_record_space[name].dtype,
to="pytorch"))
records = define_by_run_unflatten(records)
return records, indices, weights
@rlgraph_api(must_be_complete=False)
def _graph_fn_update_records(self, indices, update):
for index, loss in zip(indices, update):
priority = np.power(loss, self.alpha)
self.merged_segment_tree.insert(index, priority)
self.max_priority = max(self.max_priority, priority)
def get_state(self):
return {
"size": self.size,
"index": self.index,
"max_priority": self.max_priority
}
class ApexMemory(Specifiable):
"""
Apex prioritized replay implementing compression.
"""
def __init__(self,
state_space=None,
action_space=None,
capacity=1000,
alpha=1.0,
beta=1.0):
"""
Args:
state_space (dict): State spec.
action_space (dict): Actions spec.
capacity (int): Max capacity.
alpha (float): Initial weight.
beta (float): Prioritisation factor.
"""
super(ApexMemory, self).__init__()
self.state_space = state_space
self.action_space = action_space
self.container_actions = isinstance(action_space, dict)
self.memory_values = []
self.index = 0
self.capacity = capacity
self.size = 0
self.max_priority = 1.0
self.alpha = alpha
self.beta = beta
self.default_new_weight = np.power(self.max_priority, self.alpha)
self.priority_capacity = 1
while self.priority_capacity < self.capacity:
self.priority_capacity *= 2
# Create segment trees, initialize with neutral elements.
sum_values = [0.0 for _ in range_(2 * self.priority_capacity)]
sum_segment_tree = MemSegmentTree(sum_values, self.priority_capacity,
operator.add)
min_values = [float('inf') for _ in range_(2 * self.priority_capacity)]
min_segment_tree = MemSegmentTree(min_values, self.priority_capacity,
min)
self.merged_segment_tree = MinSumSegmentTree(
sum_tree=sum_segment_tree,
min_tree=min_segment_tree,
capacity=self.priority_capacity)
def insert_records(self, record):
# TODO: This has the record interface, but actually expects a specific structure anyway, so
# may as well change API?
if self.index >= self.size:
self.memory_values.append(record)
else:
self.memory_values[self.index] = record
# Weights. # TODO this is problematic due to index not existing.
if record[5] is not None:
self.merged_segment_tree.insert(self.index, record[5]**self.alpha)
else:
self.merged_segment_tree.insert(self.index,
self.max_priority**self.alpha)
# Update indices.
self.index = (self.index + 1) % self.capacity
self.size = min(self.size + 1, self.capacity)
def read_records(self, indices):
"""
Obtains record values for the provided indices.
Args:
indices (ndarray): Indices to read. Assumed to be not contiguous.
Returns:
dict: Record value dict.
"""
states = []
if self.container_actions:
actions = {k: [] for k in self.action_space.keys()}
else:
actions = []
rewards = []
terminals = []
next_states = []
for index in indices:
state, action, reward, terminal, next_state, weight = self.memory_values[
index]
states.append(ray_decompress(state))
if self.container_actions:
for name in self.action_space.keys():
actions[name].append(action[name])
else:
actions.append(action)
rewards.append(reward)
terminals.append(terminal)
next_states.append(ray_decompress(next_state))
if self.container_actions:
for name in self.action_space.keys():
actions[name] = np.squeeze(np.array(actions[name]))
else:
actions = np.array(actions)
return dict(states=np.asarray(states),
actions=actions,
rewards=np.asarray(rewards),
terminals=np.asarray(terminals),
next_states=np.asarray(next_states))
def get_records(self, num_records):
indices = []
prob_sum = self.merged_segment_tree.sum_segment_tree.get_sum(
0, self.size)
samples = np.random.random(size=(num_records, )) * prob_sum
for sample in samples:
indices.append(
self.merged_segment_tree.sum_segment_tree.index_of_prefixsum(
prefix_sum=sample))
sum_prob = self.merged_segment_tree.sum_segment_tree.get_sum()
min_prob = self.merged_segment_tree.min_segment_tree.get_min_value(
) / sum_prob + SMALL_NUMBER
max_weight = (min_prob * self.size)**(-self.beta)
weights = []
for index in indices:
sample_prob = self.merged_segment_tree.sum_segment_tree.get(
index) / sum_prob
weight = (sample_prob * self.size)**(-self.beta)
weights.append(weight / max_weight)
return self.read_records(
indices=indices), np.asarray(indices), np.asarray(weights)
def update_records(self, indices, update):
for index, loss in zip(indices, update):
self.merged_segment_tree.insert(index, loss**self.alpha)
self.max_priority = max(self.max_priority, loss)
