class ReplayBuffer(AbstractReplayBuffer):
def __init__(self, capacity=None):
self.memory = RandomAccessQueue(maxlen=capacity)
def append(self, state, action, reward, next_state=None, next_action=None,
is_state_terminal=False):
experience = dict(state=state, action=action, reward=reward,
next_state=next_state, next_action=next_action,
is_state_terminal=is_state_terminal)
self.memory.append(experience)
def sample(self, n):
assert len(self.memory) >= n
return self.memory.sample(n)
def __len__(self):
return len(self.memory)
def save(self, filename):
with open(filename, 'wb') as f:
pickle.dump(self.memory, f)
def load(self, filename):
with open(filename, 'rb') as f:
self.memory = pickle.load(f)
if isinstance(self.memory, collections.deque):
# Load v0.2
self.memory = RandomAccessQueue(
self.memory, maxlen=self.memory.maxlen)
def stop_current_episode(self):
pass
class ReplayBuffer(object):
def __init__(self, capacity=None):
self.memory = RandomAccessQueue(maxlen=capacity)
def append(self,
state,
action,
reward,
next_state=None,
next_action=None,
is_state_terminal=False):
"""Append a transition to this replay buffer
Args:
state: s_t
action: a_t
reward: r_t
next_state: s_{t+1} (can be None if terminal)
next_action: a_{t+1} (can be None for off-policy algorithms)
is_state_terminal (bool)
"""
experience = dict(state=state,
action=action,
reward=reward,
next_state=next_state,
next_action=next_action,
is_state_terminal=is_state_terminal)
self.memory.append(experience)
def sample(self, n):
"""Sample n unique samples from this replay buffer"""
assert len(self.memory) >= n
return self.memory.sample(n)
def __len__(self):
return len(self.memory)
def save(self, filename):
with open(filename, 'wb') as f:
pickle.dump(self.memory, f)
def load(self, filename):
with open(filename, 'rb') as f:
self.memory = pickle.load(f)
def stop_current_episode(self):
pass
class EpisodicReplayBuffer(AbstractEpisodicReplayBuffer):
def __init__(self, capacity=None):
self.current_episode = []
self.episodic_memory = RandomAccessQueue()
self.memory = RandomAccessQueue()
self.capacity = capacity
def append(self, state, action, reward, next_state=None, next_action=None,
is_state_terminal=False, **kwargs):
experience = dict(state=state, action=action, reward=reward,
next_state=next_state, next_action=next_action,
is_state_terminal=is_state_terminal,
**kwargs)
self.current_episode.append(experience)
if is_state_terminal:
self.stop_current_episode()
def sample(self, n):
assert len(self.memory) >= n
return self.memory.sample(n)
def sample_episodes(self, n_episodes, max_len=None):
assert len(self.episodic_memory) >= n_episodes
episodes = self.episodic_memory.sample(n_episodes)
if max_len is not None:
return [random_subseq(ep, max_len) for ep in episodes]
else:
return episodes
def __len__(self):
return len(self.memory)
@property
def n_episodes(self):
return len(self.episodic_memory)
def save(self, filename):
with open(filename, 'wb') as f:
pickle.dump((self.memory, self.episodic_memory), f)
def load(self, filename):
with open(filename, 'rb') as f:
memory = pickle.load(f)
if isinstance(memory, tuple):
self.memory, self.episodic_memory = memory
else:
# Load v0.2
# FIXME: The code works with EpisodicReplayBuffer
# but not with PrioritizedEpisodicReplayBuffer
self.memory = RandomAccessQueue(memory)
self.episodic_memory = RandomAccessQueue()
# Recover episodic_memory with best effort.
episode = []
for item in self.memory:
episode.append(item)
if item['is_state_terminal']:
self.episodic_memory.append(episode)
episode = []
def stop_current_episode(self):
if self.current_episode:
self.episodic_memory.append(self.current_episode)
self.memory.extend(self.current_episode)
self.current_episode = []
while self.capacity is not None and \
len(self.memory) > self.capacity:
discarded_episode = self.episodic_memory.popleft()
for _ in range(len(discarded_episode)):
self.memory.popleft()
assert not self.current_episode
class ReplayBuffer(replay_buffer.AbstractReplayBuffer):
"""Experience Replay Buffer
As described in
https://storage.googleapis.com/deepmind-media/dqn/DQNNaturePaper.pdf.
Args:
capacity (int): capacity in terms of number of transitions
num_steps (int): Number of timesteps per stored transition
(for N-step updates)
"""
def __init__(self, capacity=None, num_steps=1):
self.capacity = capacity
assert num_steps > 0
self.num_steps = num_steps
self.memory = RandomAccessQueue(maxlen=capacity)
self.last_n_transitions = collections.defaultdict(
lambda: collections.deque([], maxlen=num_steps))
def append(self,
state,
action,
reward,
next_state=None,
next_action=None,
is_state_terminal=False,
env_id=0,
**kwargs):
last_n_transitions = self.last_n_transitions[env_id]
experience = dict(state=state,
action=action,
reward=reward,
next_state=next_state,
next_action=next_action,
is_state_terminal=is_state_terminal,
**kwargs)
last_n_transitions.append(experience)
if is_state_terminal:
while last_n_transitions:
self.memory.append(list(last_n_transitions))
del last_n_transitions[0]
assert len(last_n_transitions) == 0
else:
if len(last_n_transitions) == self.num_steps:
self.memory.append(list(last_n_transitions))
def stop_current_episode(self, env_id=0):
last_n_transitions = self.last_n_transitions[env_id]
# if n-step transition hist is not full, add transition;
# if n-step hist is indeed full, transition has already been added;
if 0 < len(last_n_transitions) < self.num_steps:
self.memory.append(list(last_n_transitions))
# avoid duplicate entry
if 0 < len(last_n_transitions) <= self.num_steps:
del last_n_transitions[0]
while last_n_transitions:
self.memory.append(list(last_n_transitions))
del last_n_transitions[0]
assert len(last_n_transitions) == 0
def sample(self, num_experiences):
assert len(self.memory) >= num_experiences
return self.memory.sample(num_experiences)
def __len__(self):
return len(self.memory)
def save(self, filename):
with open(filename, 'wb') as f:
pickle.dump(self.memory, f)
def load(self, filename):
with open(filename, 'rb') as f:
self.memory = pickle.load(f)
if isinstance(self.memory, collections.deque):
# Load v0.2
self.memory = RandomAccessQueue(self.memory,
maxlen=self.memory.maxlen)
class ReplayBuffer(AbstractReplayBuffer):
def __init__(self, capacity=None, num_steps=1):
self.capacity = capacity
assert num_steps > 0
self.num_steps = num_steps
self.memory = RandomAccessQueue(maxlen=capacity)
self.last_n_transitions = collections.deque([], maxlen=num_steps)
def append(self,
state,
action,
reward,
next_state=None,
next_action=None,
is_state_terminal=False):
experience = dict(state=state,
action=action,
reward=reward,
next_state=next_state,
next_action=next_action,
is_state_terminal=is_state_terminal)
self.last_n_transitions.append(experience)
if is_state_terminal:
while self.last_n_transitions:
self.memory.append(list(self.last_n_transitions))
del self.last_n_transitions[0]
assert len(self.last_n_transitions) == 0
else:
if len(self.last_n_transitions) == self.num_steps:
self.memory.append(list(self.last_n_transitions))
def stop_current_episode(self):
# if n-step transition hist is not full, add transition;
# if n-step hist is indeed full, transition has already been added;
if 0 < len(self.last_n_transitions) < self.num_steps:
self.memory.append(list(self.last_n_transitions))
# avoid duplicate entry
if 0 < len(self.last_n_transitions) <= self.num_steps:
del self.last_n_transitions[0]
while self.last_n_transitions:
self.memory.append(list(self.last_n_transitions))
del self.last_n_transitions[0]
assert len(self.last_n_transitions) == 0
def sample(self, num_experiences):
assert len(self.memory) >= num_experiences
return self.memory.sample(num_experiences)
def __len__(self):
return len(self.memory)
def save(self, filename):
with open(filename, 'wb') as f:
pickle.dump(self.memory, f)
def load(self, filename):
with open(filename, 'rb') as f:
self.memory = pickle.load(f)
if isinstance(self.memory, collections.deque):
# Load v0.2
self.memory = RandomAccessQueue(self.memory,
maxlen=self.memory.maxlen)
class EpisodicReplayBuffer(object):
def __init__(self, capacity=None):
self.current_episode = []
self.episodic_memory = RandomAccessQueue()
self.memory = RandomAccessQueue()
self.capacity = capacity
def append(self,
state,
action,
reward,
next_state=None,
next_action=None,
is_state_terminal=False,
**kwargs):
"""Append a transition to this replay buffer
Args:
state: s_t
action: a_t
reward: r_t
next_state: s_{t+1} (can be None if terminal)
next_action: a_{t+1} (can be None for off-policy algorithms)
is_state_terminal (bool)
"""
experience = dict(state=state,
action=action,
reward=reward,
next_state=next_state,
next_action=next_action,
is_state_terminal=is_state_terminal,
**kwargs)
self.current_episode.append(experience)
if is_state_terminal:
self.stop_current_episode()
def sample(self, n):
"""Sample n unique samples from this replay buffer"""
assert len(self.memory) >= n
return self.memory.sample(n)
def sample_episodes(self, n_episodes, max_len=None):
"""Sample n unique samples from this replay buffer"""
assert len(self.episodic_memory) >= n_episodes
episodes = self.episodic_memory.sample(n_episodes)
if max_len is not None:
return [random_subseq(ep, max_len) for ep in episodes]
else:
return episodes
def __len__(self):
return len(self.episodic_memory)
def save(self, filename):
with open(filename, 'wb') as f:
pickle.dump((self.memory, self.episodic_memory), f)
def load(self, filename):
with open(filename, 'rb') as f:
self.memory, self.episodic_memory = pickle.load(f)
def stop_current_episode(self):
if self.current_episode:
self.episodic_memory.append(self.current_episode)
self.memory.extend(self.current_episode)
self.current_episode = []
while self.capacity is not None and \
len(self.memory) > self.capacity:
discarded_episode = self.episodic_memory.popleft()
for _ in range(len(discarded_episode)):
self.memory.popleft()
assert not self.current_episode
class SuccessPrioReplayBuffer(chainerrl.replay_buffer.AbstractReplayBuffer):
def __init__(self, capacity=None):
self.current_episode = []
self.current_episode_R = 0.0
self.good_episodic_memory = []
self.good_episodic_memory_capacity = 20
self.good_memory = RandomAccessQueue()
self.normal_episodic_memory = []
self.normal_episodic_memory_capacity = 50
self.normal_memory = RandomAccessQueue()
self.bad_episodic_memory = []
self.bad_episodic_memory_capacity = 10
self.bad_memory = RandomAccessQueue()
self.capacity = capacity
self.all_step_count = 0
self.episode_count = 0
def append(self,
state,
action,
reward,
next_state=None,
next_action=None,
is_state_terminal=False,
**kwargs):
experience = dict(state=state,
action=action,
reward=reward,
next_state=next_state,
next_action=next_action,
is_state_terminal=is_state_terminal,
**kwargs)
self.current_episode.append(experience)
self.current_episode_R += reward
self.all_step_count += 1
if is_state_terminal:
self.stop_current_episode()
def sample(self, n):
count_sample = 0
ans = []
if len(self.bad_memory) > 0:
n_s = min((len(self.bad_memory), n // 4))
ans.extend(self.bad_memory.sample(n_s))
count_sample += n_s
if len(self.normal_memory) > 0:
n_s = min((len(self.normal_memory), (n // 4) * 2 - count_sample))
ans.extend(self.normal_memory.sample(n_s))
count_sample += n_s
if len(self.good_memory) > 0:
n_s = min((len(self.good_memory), (n // 4) * 3 - count_sample))
ans.extend(self.good_memory.sample(n_s))
count_sample += n_s
if (count_sample < n) and (len(self.current_episode) > 0):
n_s = min((len(self.current_episode), n - count_sample))
#ans.extend(random.sample(self.current_episode, n_s))
ans.extend(self.current_episode[len(self.current_episode) - 1 -
n_s:len(self.current_episode) - 1])
return ans
def __len__(self):
return self.all_step_count
def save(self, filename):
with open(filename, 'wb') as f:
pickle.dump((self.good_episodic_memory,
self.normal_episodic_memory, self.bad_episodic_memory,
self.all_step_count, self.episode_count), f)
def load(self, filename):
with open(filename, 'rb') as f:
memory = pickle.load(f)
if isinstance(memory, tuple):
self.good_episodic_memory, self.normal_episodic_memory, self.bad_episodic_memory, self.all_step_count, self.episode_count = memory
self.good_memory = RandomAccessQueue()
for e in self.good_episodic_memory:
self.good_memory.extend(e[2])
self.normal_memory = RandomAccessQueue()
for e in self.normal_episodic_memory:
self.normal_memory.extend(e[2])
self.bad_memory = RandomAccessQueue()
for e in self.bad_episodic_memory:
self.bad_memory.extend(e[2])
self.current_episode = []
self.current_episode_R = 0.0
else:
print("bad replay file")
def stop_current_episode(self):
if self.current_episode:
new_normal_episode = None
if len(self.current_episode) > 1:
if len(self.good_episodic_memory
) >= self.good_episodic_memory_capacity:
new_normal_episode = heapq.heappushpop(
self.good_episodic_memory,
(copy.copy(self.current_episode_R),
copy.copy(self.episode_count), self.current_episode))
else:
heapq.heappush(
self.good_episodic_memory,
(copy.copy(self.current_episode_R),
copy.copy(self.episode_count), self.current_episode))
self.current_episode = []
self.episode_count += 1
new_bad_episode = None
if new_normal_episode is not None:
if len(self.normal_episodic_memory
) >= self.normal_episodic_memory_capacity:
new_bad_episode = heapq.heappushpop(
self.normal_episodic_memory, new_normal_episode)
else:
heapq.heappush(self.normal_episodic_memory,
new_normal_episode)
if new_bad_episode is not None:
if len(self.bad_episodic_memory
) >= self.bad_episodic_memory_capacity:
drop_episode = heapq.heappushpop(self.bad_episodic_memory,
new_bad_episode)
self.all_step_count -= len(drop_episode[2])
else:
heapq.heappush(self.bad_episodic_memory, new_bad_episode)
self.good_memory = RandomAccessQueue()
for e in self.good_episodic_memory:
self.good_memory.extend(e[2])
self.normal_memory = RandomAccessQueue()
for e in self.normal_episodic_memory:
self.normal_memory.extend(e[2])
self.bad_memory = RandomAccessQueue()
for e in self.bad_episodic_memory:
self.bad_memory.extend(e[2])
assert not self.current_episode
self.current_episode_R = 0.0