class Memory(object):
def __init__(self, batch_size, max_size, beta):
self.batch_size = batch_size # mini batch大小
self.max_size = 2**math.floor(math.log2(max_size)) # 保证 sum tree 为完全二叉树
self.beta = beta
self._sum_tree = SumTree(max_size)
def store_transition(self, s, a, r, s_, done):
self._sum_tree.add((s, a, r, s_, done))
def get_mini_batches(self):
n_sample = self.batch_size if self._sum_tree.size >= self.batch_size else self._sum_tree.size
total = self._sum_tree.get_total()
step = total // n_sample
points_transitions_probs = []
for i in range(n_sample):
v = np.random.uniform(i * step, (i + 1) * step - 1)
t = self._sum_tree.sample(v)
points_transitions_probs.append(t)
points, transitions, probs = zip(*points_transitions_probs)
# 计算重要性比率
max_impmortance_ratio = (n_sample * self._sum_tree.get_min())**-self.beta
importance_ratio = [(n_sample * probs[i])**-self.beta / max_impmortance_ratio
for i in range(len(probs))]
return points, tuple(np.array(e) for e in zip(*transitions)), importance_ratio
def update(self, points, td_error):
for i in range(len(points)):
self._sum_tree.update(points[i], td_error[i])
class PrioritizedReplayBuffer(ReplayBuffer):
def __init__(self, buffer_size, alpha):
self.capacity = buffer_size
self.tree = SumTree(buffer_size)
self.alpha = alpha
self.max_priority = 1
#self.beta_initial = ??
#self.beta_steps = ??
def add(self, experience):
self.tree.add(self.max_priority, experience)
def update(self, index, experience, td_error):
priority = (abs(td_error) + 0.0001)**self.alpha
self.tree.update(index, priority)
if self.max_priority < priority:
self.max_priority = priority
def sample(self, batch_size):
indexes = []
batchs = []
total = self.tree.total()
section = total / batch_size
for i in range(batch_size):
r = section * i + np.random.random() * section
(idx, priority, experience) = self.tree.get(r)
indexes.append(idx) # 後のpriority更新に使う
batchs.append(experience)
return (indexes, batchs)
class Memory:
e = 0.01
a = 0.6
def __init__(self, capacity):
self.tree = SumTree(capacity)
self.capacity = capacity
def _getPriority(self, error):
return (error + self.e)**self.a
def add(self, error, sample):
p = self._getPriority(error)
self.tree.add(p, sample)
def sample(self, n):
batch = []
segment = self.tree.total() / n
for i in range(n):
a = segment * i
b = segment * (i + 1)
s = random.uniform(a, b)
(idx, p, data) = self.tree.get(s)
batch.append((idx, data))
return batch
def update(self, idx, error):
p = self._getPriority(error)
self.tree.update(idx, p)
class PriorityMemory:
def __init__(self, capacity):
"""
Instantiate a priority based memory with capable of holding
capacity experiences. Memories are sampled with frequency
based on their priority.
"""
# Circular buffer array based tree with priorities as node values.
self.tree = SumTree(capacity)
self.e = 0.01 # Small constant to ensure all priorities > 0
self.a = 0.6 # Constant to control the weight of error on priority
def _getPriority(self, error):
"""
Convert error to a priority based on the constants "e" and "a"
"""
return (error + self.e) ** self.a
def add(self, experience, error):
"""
Add an experience to memory
"""
p = self._getPriority(error)
self.tree.add(p, experience)
def sample(self, n):
"""
Sample n experiences from memory. Experiences selection
frequency is based on priority.
Returns:
- mini_batch: Sequence containing the experiences.
- indicies: The index of the node associated with each experience
so that its priority can be updated.
"""
mini_batch = []
indicies = []
segment = self.tree.total() / n
for i in range(n):
a = segment * i
b = segment * (i + 1)
s = random.uniform(a, b)
(idx, _, experience) = self.tree.get(s)
mini_batch.append(experience)
indicies.append(idx)
return mini_batch, indicies
def update(self, idx, error):
"""
Update the priority associated with a memory.
"""
p = self._getPriority(error)
self.tree.update(idx, p)
class Memory: # stored as ( s, a, r, s_ ) in SumTree
def __init__(self,
capacity,
alpha=0.6,
beta=0.4,
beta_anneal_step=0.001,
epsilon=0.00000001):
tree_capacity = 1
while tree_capacity < size:
tree_capacity *= 2
self.tree = SumTree(capacity)
self.capacity = capacity
self.a = alpha
self.beta = beta
self.beta_increment_per_sampling = beta_anneal_step
self.e = epsilon
def _get_priority(self, error):
# Direct proportional prioritization
return (np.abs(error) + self.e)**self.a
def add(self, error, sample):
p = self._get_priority(error)
self.tree.add(p, sample)
def sample(self, n):
batch = []
idxs = []
segment = self.tree.total() / n
priorities = []
for i in range(n):
a = segment * i
b = segment * (i + 1)
data = 0
while data == 0:
s = random.uniform(a, b)
(idx, p, data) = self.tree.get(s)
priorities.append(p)
batch.append(data)
idxs.append(idx)
sampling_probabilities = priorities / self.tree.total()
is_weight = np.power(self.tree.n_entries * sampling_probabilities,
-self.beta)
is_weight /= is_weight.max()
return batch, idxs, is_weight
def step(self):
self.beta = np.min(
[1. - self.e, self.beta + self.beta_increment_per_sampling])
def update(self, idx, error):
p = self._get_priority(error)
self.tree.update(idx, p)
class MemoryDB: # stored as ( s, a, r, s_ ) in SumTree
def __init__(self, e, a, beta, beta_increment_per_sampling, capacity,
max_priority):
self.capacity = capacity
self.e = e
self.a = a
self.beta = beta
self.beta_increment_per_sampling = beta_increment_per_sampling
self.capacity = capacity
self.max_priority = max_priority
self.sum_tree = SumTree(self.capacity)
def _get_priority(self, error):
return min((self.max_priority, (error + self.e)**self.a))
def add(self, experience, error=None):
p = self._get_priority(error) if error != None else self.max_priority
self.sum_tree.add(p, experience)
def add_batch(self, experiences):
for experience in experiences:
self.add(experience, self.max_priority)
def update(self, index, error, experience):
p = self._get_priority(error)
self.sum_tree.update(index, p)
def update_batch(self, indexes, errors, experiences):
for index, error, experience in zip(indexes, errors, experiences):
self.update(index, error, experience)
def get_experiences_size(self):
return self.sum_tree.getCount()
def sample(self, n):
batch = []
idxs = []
segment = self.sum_tree.total() / n
priorities = []
self.beta = np.min([1., self.beta + self.beta_increment_per_sampling])
for i in range(n):
a = segment * i
b = segment * (i + 1)
s = random.uniform(a, b)
(idx, p, data) = self.sum_tree.get(s)
priorities.append(p)
batch.append(data)
idxs.append(idx)
sampling_probabilities = priorities / self.sum_tree.total()
is_weight = np.power(self.sum_tree.n_entries * sampling_probabilities,
-self.beta)
is_weight /= is_weight.max()
return batch, idxs, is_weight
class Memory: # stored as ( s, a, r, s_ ) in SumTree
e = 1e-10
a = 0.5
beta = 0.4
beta_increment_per_sampling = 0.001
def __init__(self, capacity):
self.tree = SumTree(capacity)
self.capacity = capacity
def _get_priority(self, error):
return (error + self.e)**self.a
def append(self, data):
error, sample = data
p = self._get_priority(error)
self.tree.add(p, sample)
def sample(self, n):
batch = []
idxs = []
segment = self.tree.total() / n
priorities = []
self.beta = np.min([1., self.beta + self.beta_increment_per_sampling])
for i in range(n):
a = segment * i
b = segment * (i + 1)
s = random.uniform(a, b)
(idx, p, data) = self.tree.get(s)
priorities.append(p)
batch.append(data)
idxs.append(idx)
sampling_probabilities = priorities / (self.tree.total() + 1e-10)
is_weight = np.power(self.tree.n_entries * sampling_probabilities,
-self.beta)
is_weight /= is_weight.max()
return batch, idxs, is_weight
def update(self, idx, error):
p = self._get_priority(error)
self.tree.update(idx, p)
def __len__(self):
return self.tree.n_entries
class PrioritizedReplayMemory:
def __init__(self, capacity, alpha=0.6, eps=1e-2):
self.tree = SumTree(capacity)
self.alpha = alpha # alpha determines how much prioritization is used
self.eps = eps # epsilon smooths priority, priority = (TD_error + eps) ** alpha
def _get_priority(self, td_error):
return (td_error + self.eps) ** self.alpha
def current_length(self):
return self.tree.current_length()
def total_sum(self):
return self.tree.total_sum()
def push(self, event, td_error):
priority = self._get_priority(td_error)
self.tree.insert(event, priority)
def sample(self, batch_sz):
batch = []
indices = []
priorities = []
segment = self.tree.total_sum() / batch_sz
for i in range(batch_sz):
l = segment * i
r = segment * (i + 1)
s = random.uniform(l, r)
(idx, priority, data) = self.tree.get(s)
batch.append(data)
indices.append(idx)
priorities.append(priority)
samples = map(np.array, zip(*batch))
return samples, indices, priorities
def update(self, idx, td_error):
if isinstance(idx, list):
for i in range(len(idx)):
priority = self._get_priority(td_error[i])
self.tree.update(idx[i], priority)
else:
priority = self._get_priority(td_error)
self.tree.update(idx, priority)
class PrioritizeReplayBuffer(ReplayBuffer):
# Based on https://github.com/y-kamiya/machine-learning-samples/blob/7b6792ce37cc69051e9053afeddc6d485ad34e79/python3/reinforcement/dqn/agent.py
EPSILON = 0.0001
ALPHA = 0.6
BETA = 0.4
size = 0
def __init__(self, capacity):
super().__init__(capacity=capacity)
self.td_error_epsilon = 0.0001
self.tree = SumTree(capacity)
def __len__(self):
return self.size
def _getPriority(self, td_error):
return (td_error + self.EPSILON)**self.ALPHA
def push(self, state, action, done, next_state, reward, p_index):
self.size += 1
transition = self.Transition(state, action, done, next_state, reward,
p_index)
priority = self.tree.max()
if priority <= 0:
priority = 1
self.tree.add(priority, transition)
def sample(self, batch_size, episode):
list = []
indexes = []
weights = np.empty(batch_size, dtype='float32')
total = self.tree.total()
beta = self.BETA + (
1 - self.BETA) * episode #episode / self.config.num_episodes
for i, rand in enumerate(np.random.uniform(0, total, batch_size)):
(idx, priority, data) = self.tree.get(rand)
list.append(data)
indexes.append(idx)
weights[i] = (self.capacity * priority / total)**(-beta)
return (indexes, list, weights / weights.max())
def update(self, idx, td_error):
priority = self._getPriority(td_error)
self.tree.update(idx, priority)
class PERMemory:
EPSILON = 0.0001
ALPHA = 0.5
BETA = 0.4
size = 0
def __init__(self, config, capacity):
self.config = config
self.capacity = capacity
self.tree = SumTree(capacity)
def _getPriority(self, td_error):
return (td_error + self.EPSILON) ** self.ALPHA
def push(self, transition):
self.size += 1
priority = self.tree.max()
if priority <= 0:
priority = 1
self.tree.add(priority, transition)
def sample(self, size, episode):
list = []
indexes = []
weights = np.empty(size, dtype='float32')
total = self.tree.total()
beta = self.BETA + (1 - self.BETA) * episode / self.config.num_episodes
beta = min(1.0, beta)
for i, rand in enumerate(np.random.uniform(0, total, size)):
(idx, priority, data) = self.tree.get(rand)
list.append(data)
indexes.append(idx)
weights[i] = (self.capacity * priority / total) ** (-beta)
return (indexes, list, weights / weights.max())
def update(self, idx, td_error):
priority = self._getPriority(td_error)
self.tree.update(idx, priority)
def __len__(self):
return self.size
class PrioritizedMemory:
e = 0.01
a = 0.6
beta = 0.4
beta_increment_per_sampling = 0.001
def __init__(self, capacity):
self.tree = SumTree(capacity)
self.capacity = capacity
def _get_priority(self, error):
return (np.abs(error) + self.e)**self.a
def push(self, error, sample):
p = self._get_priority(error)
self.tree.add(p, sample)
def sample(self, n):
batch = []
idxs = []
segment = self.tree.total() / n
priorities = []
self.beta = np.min([1., self.beta + self.beta_increment_per_sampling])
for i in range(n):
a = segment * i
b = segment * (i + 1)
s = random.uniform(a, b)
(idx, p, data) = self.tree.get(s)
priorities.append(p)
batch.append(data)
idxs.append(idx)
sampling_probabilities = priorities / self.tree.total()
is_weight = np.power(self.tree.n_entries * sampling_probabilities,
-self.beta)
is_weight /= is_weight.max()
return batch, idxs, is_weight
def update(self, idx, error):
p = self._get_priority(error)
self.tree.update(idx, p)
class Memory(object):
def __init__(self, capacity, batch_size):
self.capacity = capacity
self.batch_size = batch_size
self.tree = SumTree(capacity=capacity)
self.alpha = 0.6
self.beta = 0.4
self.p_epsilon = 1e-4
self.batch_size = 50
def _get_priority(self, priorities):
priorities += self.p_epsilon
priorities = np.minimum(priorities, 1.0)
priorities = np.power(priorities, self.alpha)
return priorities
def store(self, transition):
max_p = np.max(self.tree.tree[-self.capacity:])
if max_p == 0:
max_p = 1.0
self.tree.add(transition, max_p)
def sample(self):
avg_p = self.tree.total_p() / self.batch_size
batch_tree_idx, batch_p, batch_data = [], [], []
for i in range(self.batch_size):
a, b = avg_p * i, avg_p * (i + 1)
s = np.random.uniform(a, b)
tree_idx, p, data = self.tree.sample(s)
batch_tree_idx.append(tree_idx)
batch_p.append(p)
batch_data.append(data)
batch_p /= self.tree.total_p()
batch_weight = np.power(batch_p * self.capacity, -self.beta)
batch_weight = batch_weight / max(batch_weight)
batch_tree_idx, batch_data, batch_weight = map(
np.array, [batch_tree_idx, batch_data, batch_weight])
return batch_tree_idx, batch_data, batch_weight
def update(self, tree_idx, priorities):
priorities = self._get_priority(priorities)
for index, p in zip(tree_idx, priorities):
self.tree.update(index, p)
class Memory(object):
e = 0.05
def __init__(self, capacity, pr_scale):
self.capacity = capacity
self.memory = ST(self.capacity)
self.pr_scale = pr_scale
self.max_pr = 0
def get_priority(self, error):
return (error + self.e)**self.pr_scale
def remember(self, sample, error):
p = self.get_priority(error)
self_max = max(self.max_pr, p)
self.memory.add(self_max, sample)
def sample(self, n):
sample_batch = []
sample_batch_indices = []
sample_batch_priorities = []
num_segments = self.memory.total() / n
for i in range(n):
left = num_segments * i
right = num_segments * (i + 1)
s = random.uniform(left, right)
idx, pr, data = self.memory.get(s)
sample_batch.append((idx, data))
sample_batch_indices.append(idx)
sample_batch_priorities.append(pr)
return [sample_batch, sample_batch_indices, sample_batch_priorities]
def update(self, batch_indices, errors):
for i in range(len(batch_indices)):
p = self.get_priority(errors[i])
self.memory.update(batch_indices[i], p)
class Replay_Memory:
def __init__(self):
global MEMORY_LEN
self.tree = SumTree(MEMORY_LEN)
def add(self, error, sample):
global MEMORY_BIAS, MEMORY_POW
priority = (error + MEMORY_BIAS)**MEMORY_POW
self.tree.add(priority, sample)
def sample(self):
"""
Get a sample batch of the replay memory
Returns:
batch: a batch with one sample from each segment of the memory
"""
global BATCH_SIZE
batch = []
#we want one representative of all distribution-segments in the batch
#e.g BATCH_SIZE=2: batch contains one sample from [min,median]
#and from [median,max]
segment = self.tree.total() / BATCH_SIZE
for i in range(BATCH_SIZE):
minimum = segment * i
maximum = segment * (i + 1)
s = random.uniform(minimum, maximum)
(idx, p, data) = self.tree.get(s)
batch.append((idx, data))
return batch
def update(self, idx, error):
"""
Updates one entry in the replay memory
Args:
idx: the position of the outdated transition in the memory
error: the newly calculated error
"""
priority = (error + MEMORY_BIAS)**MEMORY_POW
self.tree.update(idx, priority)
class ReplayMemory(object):
def __init__(self, max_size, alpha, eps):
self.max_size = max_size
self.alpha = alpha
self.eps = eps
self.tree = SumTree(max_size)
self.last_idxs = None
self.size = 0
def get_batch(self, batch_size):
self.last_idxs = []
ret = []
for i in range(min(batch_size, self.size)):
s = random.random() * self.tree.total()
idx, _, data = self.tree.get(s)
ret.append(pickle.loads(zlib.decompress(data)))
self.last_idxs.append(idx)
return ret
def update(self, losses):
for i in range(len(self.last_idxs)):
self.tree.update(self.last_idxs[i],
math.pow(losses[i] + self.eps, self.alpha))
def add_element(self, new_el, loss):
self.size = min(self.max_size, self.size + 1)
p = math.pow(loss + self.eps, self.alpha)
self.tree.add(p, zlib.compress(pickle.dumps(new_el)))
def __len__(self):
return self.size
class PrioritisedReplayBuffer():
"""A prioritised replay buffer.
Creates a sum tree and uses it to stores a fixed number of experience tuples. When sampled
experiences are returned with greater priority given to those with the highest absolute TD-error.
"""
def __init__(self,
buffer_size,
alpha,
beta_zero,
beta_increment_size=0.001,
epsilon=0.1,
max_priority=1.,
seed=None):
"""Priority replay buffer initialiser.
Args:
buffer_size (int): capacity of the replay buffer.
alpha (float): priority scaling hyperparameter.
beta_zero (float): importance sampling scaling hyperparameter.
beta_increment_size (float): beta annealing rate.
epsilon (float): base priority to ensure non-zero sampling probability.
max_priority (float): initial maximum priority.
seed (int): seed for random number generator
"""
random.seed(seed)
self.sum_tree = SumTree(buffer_size)
self.memory = {}
self.experience = namedtuple(
"experience", ["state", "action", "reward", "next_state", "done"])
self.buffer_size = buffer_size
self.beta_increment_size = beta_increment_size
self.max_priority = max_priority**alpha
self.min_priority = max_priority**alpha
self.last_min_update = 0
self.alpha = alpha
self.beta = beta_zero
self.epsilon = epsilon
def add(self, state, action, reward, next_state, done):
"""Creates experience tuple and adds it to the replay buffer."""
experience = self.experience(state, action, reward, next_state, done)
current_tree_idx = self.sum_tree.input_pointer
self.memory[current_tree_idx] = experience
self.sum_tree.add(self.max_priority)
def sample(self, batch_size):
"""Returns a batch of experiences sampled according to their priority."""
idx_list = []
weights = []
states = []
actions = []
rewards = []
next_states = []
done_list = []
segment = self.sum_tree.total() / batch_size
sample_list = [
random.uniform(segment * i, segment * (i + 1))
for i in range(batch_size)
]
max_weight = self.min_priority**(-self.beta)
for s in sample_list:
idx, priority = self.sum_tree.sample(s)
idx_list.append(idx)
weight = priority**(-self.beta) / max_weight
weights.append(weight)
sample = self.memory[idx]
state, action, reward, next_state, done = sample
states.append(state)
actions.append(action)
rewards.append(reward)
next_states.append(next_state)
done_list.append(done)
return states, actions, rewards, next_states, done_list, idx_list, weights
def update(self, idx_list, td_error):
"""Updates a specifics experience's priority."""
priority_list = (td_error + self.epsilon)**self.alpha
self.max_priority = max(self.max_priority, priority_list.max())
list_min_priority = priority_list.min()
if list_min_priority <= self.min_priority:
self.min_priority = list_min_priority
self.last_min_update = 0
else:
self.last_min_update += 1
if self.last_min_update >= self.buffer_size:
self.min_priority = np.array([
node.val
for node in self.sum_tree.tree_array[-self.buffer_size:]
]).min()
self.last_min_update = 0
for i, idx in enumerate(idx_list):
priority = min(self.max_priority, priority_list[i])
self.sum_tree.update(idx, priority)
self.beta = min(1, self.beta + self.beta_increment_size)
def __len__(self, ):
"""Return number of experiences in the replay buffer."""
return len(self.memory)
class MemoryDB: # stored as ( s, a, r, s_ ) in SumTree
e = 0.01
a = 0.6
beta = 0.4
beta_increment_per_sampling = 0.001
capacity = 100000
max_priority = 1
def __init__(self, host_name, db_name, collection_name):
self.host_name = host_name
self.db_name = db_name
self.collection_name = collection_name
self.client = MongoClient(host_name, 27017)
self.db = self.client[db_name]
self.replay_memory_collection = self.db[collection_name]
self.sum_tree = SumTree(self.capacity)
memory_priorities = self.replay_memory_collection.find({},
{"priority": 1})
for memory_priority in memory_priorities:
self.sum_tree.add(memory_priority["priority"],
{"_id": memory_priority["_id"]})
def retrieve_by_id(self, id):
db_experiences = self.replay_memory_collection.find({"_id": id})
return {
**_pickle.loads(db_experiences[0]['binary'], encoding='latin1'), "_id":
id
}
def _get_priority(self, error):
return (error + self.e)**self.a
def add(self, error, experience):
p = self._get_priority(error)
experience_to_save = {}
experience_to_save["terminal"] = experience["terminal"]
experience_to_save["action_index"] = experience["action_index"]
experience_to_save["actual_reward"] = experience["actual_reward"]
experience_to_save["priority"] = self.max_priority
experience_to_save["binary"] = _pickle.dumps(experience)
id = self.replay_memory_collection.insert(experience_to_save)
self.sum_tree.add(p, {"_id": id})
def add_batch(self, experiences):
for experience in experiences:
self.add(self.max_priority, experience)
def update(self, index, error, experience):
p = self._get_priority(error)
self.replay_memory_collection.update_one({"_id": experience["_id"]},
{"$set": {
"priority": p
}})
self.sum_tree.update(index, p)
def update_batch(self, indexes, errors, experiences):
for index, error, experience in zip(indexes, errors, experiences):
self.update(index, error, experience)
def get_experiences_size(self):
return self.replay_memory_collection.count()
def sample(self, n):
batch = []
idxs = []
segment = self.sum_tree.total() / n
priorities = []
self.beta = np.min([1., self.beta + self.beta_increment_per_sampling])
for i in range(n):
a = segment * i
b = segment * (i + 1)
s = random.uniform(a, b)
(idx, p, data) = self.sum_tree.get(s)
priorities.append(p)
experience = self.retrieve_by_id(data["_id"])
batch.append(experience)
print(
"action index: ",
experience["action_index"],
"reward: ",
experience["actual_reward"],
"priority: ",
experience["priority"],
)
idxs.append(idx)
sampling_probabilities = priorities / self.sum_tree.total()
is_weight = np.power(self.sum_tree.n_entries * sampling_probabilities,
-self.beta)
is_weight /= is_weight.max()
return batch, idxs, is_weight
class ReplayBuffer:
def __init__(self, params):
buffer_size = params['buffer_size']
batch_size = params['batch_size']
mode = params['mode']
self.__buffer_size = buffer_size
self.__batch_size = batch_size
self.__mode = mode
self.__experience = namedtuple(
"Experience",
field_names=["state", "action", "reward", "next_state", "done"])
self.__memory = SumTree(buffer_size)
self.__memory_buffer = []
def get_batch_size(self):
return self.__batch_size
def is_ready(self):
return len(self) >= self.__batch_size
def add(self, state, action, reward, next_state, done):
self.__memory_buffer.append(
self.__experience(state, action, reward, next_state, done))
def sample(self):
buf_len = len(self.__memory_buffer)
mem_len = self.__batch_size - buf_len
experiences = []
indices = []
probs = []
# if self.__mode['PER']:
if mem_len:
#segment = self.__memory.total() / mem_len
for i in range(mem_len):
#s = random.uniform(segment * i, segment * (i + 1))
s = random.uniform(0, self.__memory.total())
idx, p, e = self.__memory.get(s)
experiences.append(e)
indices.append(idx)
probs.append(p / self.__memory.total())
for e in self.__memory_buffer:
# Add experience to the buffer and record its index
experiences.append(e)
#if self.__mode['PER']:
idx = self.__memory.add(0.0, e) # Default value for p is 0
indices.append(idx)
probs.append(1 / len(self))
self.__memory_buffer.clear()
states = torch.from_numpy(
np.vstack([e.state for e in experiences
if e is not None])).float().to(device)
actions = torch.from_numpy(
np.vstack([e.action for e in experiences
if e is not None])).long().to(device)
rewards = torch.from_numpy(
np.vstack([e.reward for e in experiences
if e is not None])).float().to(device)
next_states = torch.from_numpy(
np.vstack([e.next_state for e in experiences
if e is not None])).float().to(device)
dones = torch.from_numpy(
np.vstack([e.done for e in experiences
if e is not None]).astype(np.uint8)).float().to(device)
return states, actions, rewards, next_states, dones, indices, probs
def update(self, indices, p_values):
for idx, p in zip(indices, p_values):
self.__memory.update(idx, p)
def __len__(self):
return max(len(self.__memory), len(self.__memory_buffer))
class PrioritizeReplayBuffer(ReplayBuffer):
"""Prioritize experience replay."""
def __init__(
self,
buffer_size,
batch_size,
seed,
beta_start=0.4,
delta_beta=1e-5,
alpha=0.6,
eps=1e-8,
):
"""Initialize PER.
Args:
buffer_size (int): Size of replay buffer. The actual size will be the
first power of 2 greater than buffer_size.
batch_size (int): Size of batches to draw.
seed (float): Seed.
beta_start (float): Initial value for beta (importance sampling exponent)
delta_beta (float): Beta increment at each time step.
alpha (float): Priority exponent.
eps (float): Small positive number to avoid unsampling 0 prioritized examples.
"""
# Depth of sum tree
depth = int(math.log2(buffer_size)) + 1
super(PrioritizeReplayBuffer, self).__init__(2**depth, batch_size,
seed)
# Initialize sum tree to keep track of the sum of priorities
self.priorities = SumTree(depth)
# Current max priority
self.max_p = 1.0
# PER Parameters
self.alpha = alpha
self.eps = eps
self.beta = beta_start
self.delta_beta = delta_beta
def add(self, state, action, reward, next_state, done):
"""Add transition inside the Replay buffer."""
# Add in the sum tree with current max priority
self.priorities.add(self.max_p, self.index)
super().add(state, action, reward, next_state, done)
def sample(self):
"""Get sample."""
# Get indices to sample from sum tree
# Store these indices to compute importance sampling later
self.last_indices = self.priorities.sample(self.batch_size)
# Return transitions corresponding to this indices
return [self.data[i] for i in self.last_indices]
def update_priorities(self, td_error):
"""Update priorities."""
# Compute new priorites
new_priorities = (abs(td_error) + self.eps)**self.alpha
# Update sum tree
self.priorities.update(self.last_indices, new_priorities)
# Update the current max priority
self.max_p = max(self.max_p, max(new_priorities))
def importance_sampling(self):
"""Compute importance sampling weights of last sample."""
# Get probabilities
probs = self.priorities.get(
self.last_indices) / self.priorities.total_sum
# Compute weights
weights = (len(self) * probs)**(-self.beta)
weights /= max(weights)
# Update beta
self.beta = min(self.beta + self.delta_beta, 1)
# Return weights
return weights
class PrioritizedExperienceReplayBuffer:
"""Fixed-size buffer to store experience tuples."""
alpha = 0.6
beta = 0.4
beta_increment_per_sample = 0.001
epsilon = 1e-6
def __init__(self, action_size, buffer_size, batch_size, seed):
"""Initialize a ReplayBuffer object.
Params
======
action_size (int): dimension of each action
buffer_size (int): maximum size of buffer
batch_size (int): size of each training batch
seed (int): random seed
"""
self.action_size = action_size
self.memory = SumTree(buffer_size)
self.batch_size = batch_size
self.experience = namedtuple("Experience", field_names=["state", "action", "reward", "next_state", "done"])
self.seed = random.seed(seed)
def compute_priority(self, td_error):
return (td_error + self.epsilon) ** self.alpha
def add(self, state, action, reward, next_state, done):
"""Add a new experience to memory."""
experience = self.experience(state, action, reward, next_state, done)
max_priority = np.max(self.memory.tree[-self.memory.capacity:])
if max_priority == 0:
max_priority = 1.
self.memory.add(max_priority, experience)
def update(self, index, td_error):
priority = self.compute_priority(td_error)
self.memory.update(index, priority)
def sample(self):
"""
:return: importance weights, indices of sampled experiences, and sampled batch of experiences
"""
self.beta = np.minimum(1., self.beta + self.beta_increment_per_sample)
segment = self.memory.total() / self.batch_size
indexes = []
priorities = []
experiences = []
for i in range(self.batch_size):
# pick a segment
a = segment * i
b = segment * (i + 1)
s = np.random.uniform(a, b)
index, priority, experience = self.memory.get(s)
indexes.append(index)
priorities.append(priority)
experiences.append(experience)
sampling_probs = np.divide(priorities, self.memory.total())
# importance sampling
i_s_weights = (self.batch_size * sampling_probs) ** -self.beta
i_s_weights /= np.max(i_s_weights)
states = torch.from_numpy(np.vstack([e.state for e in experiences if e is not None])).float().to(device)
actions = torch.from_numpy(np.vstack([e.action for e in experiences if e is not None])).long().to(device)
rewards = torch.from_numpy(np.vstack([e.reward for e in experiences if e is not None])).float().to(device)
next_states = torch.from_numpy(np.vstack([e.next_state for e in experiences if e is not None])).float().to(
device)
dones = torch.from_numpy(np.vstack([e.done for e in experiences if e is not None]).astype(np.uint8)).float().to(
device)
return i_s_weights, indexes, (states, actions, rewards, next_states, dones)
def __len__(self):
"""Return the current size of internal memory."""
return self.memory.count
class PrioritizedReplayBuffer:
"""Fixed-size buffer to store experience tuples."""
def __init__(self, buffer_size, seed):
"""Initialize a ReplayBuffer object.
Params
======
seed (int): random seed
"""
self.memory = SumTree(buffer_size)
self.experience = namedtuple("Experience", field_names=["state", "action", "reward", "next_state", "done"])
self.seed = random.seed(seed)
# epsilon: small amount to avoid zero priority
# alpha: [0~1] determines how much prioritization is used. with 0, we would get the uniform case
# beta: Controls importance-sampling compensation. fully compensates for the non-uniform probabilities
# when beta=1. The unbiased nature of the updates is most important near convergence at the end of
# training, so we define a schedule on the exponent beta that starts from initial value and reaches 1
# only at the end of learning.
self.epsilon = 0.01
self.alpha = 0.6
beta_start = 0.4
self.beta_end = 1.0
self.beta = beta_start
beta_increments = 200
self.beta_increment = (self.beta_end - beta_start)/beta_increments
def add(self, state, action, reward, next_state, done):
"""Add a new experience to memory."""
experience = self.experience(state, action, reward, next_state, done)
p = self.memory.max_p()
if p == 0:
p = 1.0
self.memory.add(p=p, data=experience)
def sample(self, n):
"""Randomly sample a batch of experiences from memory."""
experiences = []
indices = []
priorities = []
segment = self.memory.total_p() / n
for i in range(n):
a = segment * i
b = segment * (i + 1)
s = random.uniform(a, b)
(idx, p, experience) = self.memory.get(s)
experiences.append(experience)
indices.append(idx)
priorities.append(p)
priorities = np.array(priorities, dtype=np.float64)
indices = np.array(indices, dtype=np.int32)
# print(f"priorities: {priorities}")
probs = priorities / self.memory.total_p()
# print(f"probs: {probs}")
# importance-sampling (IS) weights
w_is = (self.memory.capacity * probs) ** (-self.beta)
# print(f"w_IS: {w_IS}")
w_is_normalized = w_is/w_is.max()
# print(f"w_IS_normalized: {w_IS_normalized}")
# w_is_normalized = torch.from_numpy(w_is_normalized).float().to(self.device)
return experiences, indices, w_is_normalized
def update_errors(self, indices, errors):
priorities = [self._to_priority(e) for e in errors]
for (idx, p) in zip(indices, priorities):
self.memory.update(idx, p)
def _to_priority(self, error):
return (error + self.epsilon) ** self.alpha
def increase_beta(self):
if self.beta < self.beta_end:
self.beta = min(self.beta_end, self.beta + self.beta_increment)
def __len__(self):
return len(self.memory)
class Memory(object):
"""
This SumTree code is modified version and the original code is from:
https://github.com/jaara/AI-blog/blob/master/Seaquest-DDQN-PER.py
"""
beta = MEMORY_BETA
def __init__(self):
self.limit = MEMORY_CAPACITY
self.err_tree = SumTree(MEMORY_CAPACITY)
self.action_shape = (0, MEMORY_ACTION_CNT)
self.reward_shape = (0, MEMORY_REWARD_CNT)
self.terminal_shape = self.action_shape
self.observation_shape = (0, MEMORY_CRITIC_FEATURE_NUM)
self.store_times = 0
self.Transition = namedtuple(
'Transition',
('state', 'action', 'reward', 'next_state', 'terminal'))
def size(self):
return self.limit if self.store_times > self.limit else self.store_times
def sample(self, batch_size):
idxes = np.empty(self.reward_shape, dtype=np.int32)
isw = np.empty(self.reward_shape, dtype=np.float32)
obs0 = np.empty(self.observation_shape, dtype=np.float32)
obs1 = np.empty(self.observation_shape, dtype=np.float32)
actions = np.empty(self.action_shape, dtype=np.float32)
rewards = np.empty(self.reward_shape, dtype=np.float32)
terminals = np.empty(self.terminal_shape, dtype=np.bool)
nan_state = np.array([np.nan] * self.observation_shape[1])
self.beta = np.min([1., self.beta + MEMORY_BETA_INC_RATE]) # max = 1
max_td_err = np.max(self.err_tree.tree[-self.err_tree.capacity:])
idx_set = set()
for i in range(
batch_size * 2
): # sample maximum batch_size * 2 times to get batch_size different instances
v = np.random.uniform(0, self.err_tree.total_p)
idx, td_err, trans = self.err_tree.get_leaf(v)
if batch_size == len(idx_set):
break
if idx not in idx_set:
idx_set.add(idx)
else:
continue
if (trans.state == 0).all():
continue
idxes = np.row_stack((idxes, np.array([idx])))
isw = np.row_stack((isw,
np.array([
np.power(
self._getPriority(td_err) / max_td_err,
-self.beta)
])))
obs0 = np.row_stack((obs0, trans.state))
obs1 = np.row_stack(
(obs1,
nan_state if trans.terminal.all() else trans.next_state))
actions = np.row_stack((actions, trans.action))
rewards = np.row_stack((rewards, trans.reward))
terminals = np.row_stack((terminals, trans.terminal))
result = {
'obs0': array_min2d(obs0),
'actions': array_min2d(actions),
'rewards': array_min2d(rewards),
'obs1': array_min2d(obs1),
'terminals': array_min2d(terminals),
}
return idxes, result, isw
def _getPriority(self, error):
return (error + EPSILON)**MEMORY_ALPHA
def append(self, obs0, action, reward, obs1, terminal, err, training=True):
if not training:
return
trans = self.Transition(obs0, action, reward, obs1, terminal)
self.err_tree.add(self._getPriority(err), trans)
self.store_times += 1
def batch_update(self, tree_idx, errs):
errs = np.abs(errs) + EPSILON # convert to abs and avoid 0
ps = np.power(errs, MEMORY_ALPHA)
for ti, p in zip(tree_idx, ps):
self.err_tree.update(ti, p[0])
@property
def nb_entries(self):
return self.store_times
class ReplayMemory:
def __init__(self, memory_size):
self.memory_size = memory_size
self.memory = SumTree(memory_size)
self.epsilon = 0.0001 # small amount to avoid zero priority
self.alpha = 0.6 # adj_pri = pri^alpha
self.beta = 0.4 # importance-sampling, from initial value increasing to 1
self.beta_max = 1
self.beta_increment_per_sampling = 0.001
self.abs_err_upper = 1. # clipped td error
def add(self, row):
max_p = np.max(
self.memory.tree[-self.memory.capacity:]) # max adj_pri of leaves
if max_p == 0:
max_p = self.abs_err_upper
self.memory.add(max_p, row) # set the max adj_pri for new adj_pri
def get_batch(self, batch_size):
leaf_idx, batch_memory, ISWeights = np.empty(
batch_size,
dtype=np.int32), np.empty(batch_size,
dtype=object), np.empty(batch_size)
pri_seg = self.memory.total_p / batch_size # adj_pri segment
self.beta = np.min(
[self.beta_max,
self.beta + self.beta_increment_per_sampling]) # max = 1
# Pi = Prob(i) = softmax(priority(i)) = adj_pri(i) / ∑_i(adj_pri(i))
# ISWeight = (N*Pj)^(-beta) / max_i[(N*Pi)^(-beta)] = (Pj / min_i[Pi])^(-beta)
min_prob = np.min(
self.memory.tree[self.memory.capacity - 1:self.memory.capacity -
1 + self.memory.counter]) / self.memory.total_p
for i in range(batch_size):
# sample from each interval
a, b = pri_seg * i, pri_seg * (i + 1) # interval
v = np.random.uniform(a, b)
idx, p, data = self.memory.get_leaf(v)
prob = p / self.memory.total_p
ISWeights[i] = np.power(prob / min_prob, -self.beta)
leaf_idx[i], batch_memory[i] = idx, data
return leaf_idx, batch_memory, ISWeights
def update_sum_tree(self, tree_idx, td_errors):
for ti, td_error in zip(tree_idx, td_errors):
p = self._calculate_priority(td_error)
self.memory.update(ti, p)
def _calculate_priority(self, td_error):
priority = abs(td_error) + self.epsilon
clipped_pri = np.minimum(priority, self.abs_err_upper)
return np.power(clipped_pri, self.alpha)
@property
def length(self):
return self.memory.counter
def load_memory(self, memory):
self.memory = memory
def get_memory(self):
return self.memory
class PriorityMemory(SimpleMemory):
PER_e = 0.01 # Hyperparameter that we use to avoid some experiences to have 0 probability of being taken
PER_a = 0.6 # Hyperparameter that we use to make a tradeoff between taking only exp with high priority and sampling randomly
PER_b = 0.4 # importance-sampling, from initial value increasing to 1
PER_b_increment_per_sampling = 0.001
absolute_error_upper = 1. # clipped abs error
def __init__(self, obs_dim, act_dim, size, act_dtype):
SimpleMemory.__init__(self, obs_dim, act_dim, size, act_dtype)
self.tree = SumTree(size)
self.tree_lock = Lock()
def store(self, obs, act, rew, next_obs, done):
# Find the max priority
max_priority = np.max(self.tree.tree[-self.tree.capacity:])
# If the max priority = 0 we can't put priority = 0 since this exp will never have a chance to be selected
# So we use a minimum priority
if max_priority == 0:
max_priority = self.absolute_error_upper
insertion_pos = super().store(obs, act, rew, next_obs, done)
self.tree_lock.acquire()
insertion_pos_tree = self.tree.add(
max_priority) # set the max p for new p
self.tree_lock.release()
assert insertion_pos == insertion_pos_tree
def sample_batch(self, batch_size):
#idxs = np.random.randint(0, self._size, size=batch_size)
#return self.obs1_buf[idxs],self.acts_buf[idxs],self.rews_buf[idxs],self.obs2_buf[idxs],self.done_buf[idxs]
mem_idxs, tree_idxs, b_ISWeights =\
np.empty((batch_size,), dtype=np.int32),\
np.empty((batch_size,), dtype=np.int32),\
np.empty((batch_size, 1), dtype=np.float32)
# Calculate the priority segment
# Here, as explained in the paper, we divide the Range[0, ptotal] into n ranges
priority_segment = self.tree.total_priority / batch_size # priority segment
# Here we increasing the PER_b each time we sample a new minibatch
self.PER_b = np.min(
[1., self.PER_b + self.PER_b_increment_per_sampling]) # max = 1
# Calculating the max_weight
#print('### pp: {}'.format(-self.tree.capacity))
#print('### pp: {}'.format(self.tree.tree[-self.tree.capacity:]))
#print('### pp: {}'.format(np.min(self.tree.tree[-self.tree.capacity:])))
#p_min = np.min(self.tree.tree[-self.tree.capacity:]) / self.tree.total_priority
p_min = self.tree.p_min
assert p_min > 0
max_weight = (p_min * batch_size)**(-self.PER_b)
assert max_weight > 0
for i in range(batch_size):
"""
A value is uniformly sample from each range
"""
a, b = priority_segment * i, priority_segment * (i + 1)
value = np.random.uniform(a, b)
"""
Experience that correspond to each value is retrieved
"""
assert self.tree.data_pointer > 0
self.tree_lock.acquire()
index, priority = self.tree.get_leaf(value)
self.tree_lock.release()
assert priority > 0, "### index {}".format(index)
#P(j)
sampling_probabilities = priority / self.tree.total_priority
# IS = (1/N * 1/P(i))**b /max wi == (N*P(i))**-b /max wi
b_ISWeights[i, 0] = batch_size * sampling_probabilities
assert b_ISWeights[i, 0] > 0
b_ISWeights[i, 0] = np.power(b_ISWeights[i, 0], -self.PER_b)
b_ISWeights[i, 0] = b_ISWeights[i, 0] / max_weight
mem_idxs[i] = index - self.max_size + 1
tree_idxs[i] = index
#assert b_idx[i] < self.max_size , "{} and {}".format(b_idx[i], self.max_size)
return self.obs1_buf[mem_idxs],\
self.acts_buf[mem_idxs],\
self.rews_buf[mem_idxs],\
self.obs2_buf[mem_idxs],\
self.done_buf[mem_idxs],\
tree_idxs,\
b_ISWeights
"""
Update the priorities on the tree
"""
def batch_update(self, tree_idx, abs_errors):
abs_errors += self.PER_e # convert to abs and avoid 0
clipped_errors = np.minimum(abs_errors, self.absolute_error_upper)
ps = np.power(clipped_errors, self.PER_a)
self.tree_lock.acquire()
for ti, p in zip(tree_idx, ps):
self.tree.update(ti, p)
self.tree_lock.release()
class PrioritisedReplayBuffer:
def __init__(self, action_size, buffer_size, batch_size, alpha, epsilon):
self.action_size = action_size
self.tree = SumTree(buffer_size)
self.batch_size = batch_size
self.experience = namedtuple(
"Experience",
field_names=["state", "action", "reward", "next_state", "done"])
self.alpha = alpha
self.epsilon = epsilon
def add(self, error, state, action, reward, next_state, done):
e = self.experience(state, action, reward, next_state, done)
p = self._get_priority(error)
self.tree.add(p, e)
def sample(self, beta):
segment = self.tree.total(
) / self.batch_size # split into segments so we don't end up with duplicates innit
experiences = []
priorities = []
idxs = []
for i in range(self.batch_size):
start = segment * i
end = segment * (i + 1)
s = random.uniform(start, end)
idx, p, e = self.tree.get(s)
if e:
priorities.append(p)
experiences.append(e)
idxs.append(idx)
probs = priorities / self.tree.total() # big P
weights = np.power(self.tree.n_entries * probs, -beta)
weights /= weights.max() # scale so max weight is 1
states = torch.from_numpy(np.vstack([e.state for e in experiences
])).float().to(device)
actions = torch.from_numpy(np.vstack([e.action for e in experiences
])).long().to(device)
rewards = torch.from_numpy(np.vstack([e.reward for e in experiences
])).float().to(device)
next_states = torch.from_numpy(
np.vstack([e.next_state for e in experiences])).float().to(device)
dones = torch.from_numpy(
np.vstack([e.done for e in experiences
]).astype(np.uint8)).float().to(device)
weights = torch.from_numpy(weights).float().to(device)
return (states, actions, rewards, next_states, dones, weights, idxs)
def update(self, idx, error):
p = self._get_priority(error)
self.tree.update(idx, p)
def _get_priority(self, error):
return (np.abs(error) + self.epsilon)**self.alpha
def __len__(self):
"""Return the current size of internal memory."""
return self.tree.n_entries
class PriorityBuffer:
# Inspired by implementation from: https://github.com/rlcode/per/blob/master/prioritized_memory.py
def __init__(self, action_size, agent_config):
"""Initialize a PriorityBuffer object.
Params
======
action_size (int): dimension of each action
buffer_size (int): maximum size of buffer
batch_size (int): size of each training batch
seed (int): random seed
a (float): amount of uniformity in the sampling (0 == uniform, 1. == priority only)
beta_start (float): start of beta value for prioritised buffer
beta_max_steps (int): max number of steps to reach beta value of 1.
"""
self.action_size = action_size
self.tree = SumTree(capacity=agent_config.buffer_size)
self.batch_size = agent_config.batch_size
# self.seed = random.seed(buffer_config.seed)
self.epsilon = agent_config.buffer_epsilon
# how much randomness we require a = 0 (pure random) a = 1 (only priority)
self.alpha = agent_config.alpha
self.beta = agent_config.beta_start
self.beta_start = agent_config.beta_start
self.beta_end = agent_config.beta_end
self.beta_increment_per_sampling = (self.beta_end - self.beta_start) / agent_config.beta_max_steps
def add(self, sample, error):
"""Add a new experience to memory."""
p = self._get_priority(error)
state, action, reward, next_state, done = sample
e = Experience(state, action, reward, next_state, done)
self.tree.add(p, e)
def _get_priority(self, error):
return (abs(error) + self.epsilon) ** self.alpha
def sample(self):
experiences = []
idxs = []
segment = self.tree.total() / self.batch_size
priorities = []
for i in range(self.batch_size):
a = segment * i
b = segment * (i + 1)
s = random.uniform(a, b)
(idx, p, data) = self.tree.get(s)
if isinstance(data, Experience):
priorities.append(p)
experiences.append(data)
idxs.append(idx)
else:
print("WHAT THE HECK !!!")
sampling_probabilities = priorities / self.tree.total()
is_weight = np.power(self.tree.n_entries * sampling_probabilities, -self.beta)
is_weight /= is_weight.max()
states = torch.from_numpy(np.vstack([e.state for e in experiences if e is not None])).float().to(device)
actions = torch.from_numpy(np.vstack([e.action for e in experiences if e is not None])).long().to(device)
rewards = torch.from_numpy(np.vstack([e.reward for e in experiences if e is not None])).float().to(device)
next_states = torch.from_numpy(np.vstack([e.next_state for e in experiences if e is not None])).float().to(
device)
dones = torch.from_numpy(np.vstack([e.done for e in experiences if e is not None]).astype(np.uint8)).float().to(
device)
self.beta = np.min([self.beta_end, self.beta + self.beta_increment_per_sampling])
return (states, actions, rewards, next_states, dones), idxs, is_weight
def update(self, idx, error):
# Not required in normal ReplayBuffer
self.tree.update(idx, self._get_priority(error))
def __len__(self):
"""Return the current size of internal memory."""
return len(self.tree)
class Memory(object):
def __init__(self,
capacity,
state_size=37,
epsilon=0.001,
alpha=0.4,
beta=0.3,
beta_increment_per_sampling=0.001,
abs_err_upper=1):
self.tree = SumTree(capacity)
self.epsilon = epsilon # Avoid 0 priority and hence a do not give a chance for the priority to be selected stochastically
self.alpha = alpha # Vary priority vs randomness. alpha = 0 pure uniform randomnes. Alpha = 1, pure priority
self.beta = beta # importance-weight-sampling, from small to big to give more importance to corrections done towards the end of the training
self.beta_increment_per_sampling = 0.001
self.abs_err_upper = 1 # clipped abs error
self.state_size = state_size
# Save experience in memory
def store(self, state, action, reward, next_state, done):
transition = [state, action, reward, next_state, done]
max_p = np.max(self.tree.tree[-self.tree.capacity:])
# In case of no priority, we set abs error to 1
if max_p == 0:
max_p = self.abs_err_upper
self.tree.add(max_p, transition) # set the max p for new p
# Sample n amount of experiences using prioritized experience replay
def sample(self, n):
b_idx = np.empty((n, ), dtype=np.int32)
states = np.empty((n, self.state_size))
actions = np.empty((n, ))
rewards = np.empty((n, ))
next_states = np.empty((n, self.state_size))
dones = np.empty((n, ))
ISWeights = np.empty((n, )) # IS -> Importance Sampling
pri_seg = self.tree.total_p / n # priority segment
self.beta = np.min([
1., self.beta + self.beta_increment_per_sampling
]) # Increase the importance of the sampling for ISWeights
# min_prob = np.min(self.tree.tree[-self.tree.capacity:]) / self.tree.total_p # for later calculate ISweight
for i in range(n):
a, b = pri_seg * i, pri_seg * (i + 1)
v = np.random.uniform(a, b)
idx, p, data = self.tree.get_leaf(v)
prob = p / self.tree.total_p
ISWeights[i] = np.power(prob, -self.beta)
b_idx[i] = idx
states[i, :] = data[0]
actions[i] = data[1]
rewards[i] = data[2]
next_states[i, :] = data[3]
dones[i] = data[4]
states = torch.from_numpy(np.vstack(states)).float().to(device)
actions = torch.from_numpy(np.vstack(actions)).long().to(device)
rewards = torch.from_numpy(np.vstack(rewards)).float().to(device)
next_states = torch.from_numpy(
np.vstack(next_states)).float().to(device)
dones = torch.from_numpy(np.vstack(dones).astype(
np.uint8)).float().to(device)
ISWeights = torch.from_numpy(np.vstack(ISWeights)).float().to(device)
return b_idx, states, actions, rewards, next_states, dones, ISWeights
# Update the priorities according to the new errors
def batch_update(self, tree_idx, abs_errors):
abs_errors += self.epsilon # convert to abs and avoid 0
clipped_errors = np.minimum(abs_errors, self.abs_err_upper)
ps = np.power(clipped_errors, self.alpha)
for ti, p in zip(tree_idx, ps):
self.tree.update(ti, p)
def __len__(self):
return self.tree.length()
class PrioritizedReplayBuffer:
"""
Memory buffer responsible for Prioritized Experience Replay.
This buffer stores up to memory_size experiences in a circular
array-like data structure. Each experience is also associated
with a probability weight.
Batches may be sampled (with replacement) from this implied
probability distribution in batches. The provided weights should
be non-negative, but are not required to add up to 1.
"""
def __init__(self, device, memory_size, update_every=4, seed=0):
""" Initializes the data structure
:param device: (torch.device) Object representing the device where to allocate tensors
:param memory_size: (int) Maximum capacity of memory buffer
:param update_every: (int) Number of steps between update operations
:param seed: (int) Seed used for PRNG
"""
self.device = device
self.probability_weights = SumTree(capacity=memory_size, seed=seed)
self.elements = deque(maxlen=memory_size)
self.update_every = update_every
self.step = 0
self.experience = namedtuple("Experience", field_names=["state", "action", "reward", "next_state", "done"])
def add(self, state, action, reward, next_state, done):
""" Adds a experience tuple (s, a, r, s', done) to memory
:param state: (array-like) State value from experience tuple
:param action: (int) Action value from experience tuple
:param reward: (float) Reward value from experience tuple
:param next_state: (array-like) Next state value from experience tuple
:param done: (bool) Done flag from experience tuple
"""
e = self.experience(state, action, reward, next_state, done)
self.elements.append(e)
self.step += 1
# Add batch of experiences to memory, with max initial weight
if self.step >= self.update_every:
self.probability_weights.add(self.step)
self.step = 0
def sample(self, batch_size, alpha, beta):
""" Samples a batch of examples with replacement from the buffer.
:param batch_size: (int) Number of samples to sample
:param alpha: (float) PER probability hyperparameter
:param beta: (float) PER probability hyperparameter
:return:
states: (list) States from sampled experiences
actions: (list) Actions from sampled experiences
rewards: (list) Rewards from sampled experiences
next_states: (list) Next states from sampled experiences
dones: (list) Done flags from sampled experiences
indexes: (list) Indexes of sampled experiences
"""
indexes = self.probability_weights.sample(batch_size=batch_size, alpha=alpha, beta=beta)
experiences = [self.elements[i] for i in indexes]
# Copy experience tensors to device
states = torch.from_numpy(np.vstack([e.state for e in experiences])).float().to(self.device)
actions = torch.from_numpy(np.vstack([e.action for e in experiences])).long().to(self.device)
rewards = torch.from_numpy(np.vstack([e.reward for e in experiences])).float().to(self.device)
next_states = torch.from_numpy(np.vstack([e.next_state for e in experiences])).float().to(self.device)
dones = torch.from_numpy(np.vstack([e.done for e in experiences]).astype(np.uint8)).float().to(self.device)
return states, actions, rewards, next_states, dones, indexes
def update(self, indexes, weights):
""" Updates the probability weights associated with the provided indexes.
:param indexes: (array indexes) Indexes to have weights updated
:param weights: (list) New weights for the provided indexes
"""
self.probability_weights.update(indexes, weights)
def __len__(self):
"""Return the current size of internal memory."""
return len(self.probability_weights)