def load_data(self, buffer_filename):
with open(buffer_filename, 'rb') as file:
parameters = pickle.load(file)
self.buffer, self.curr_write_idx, \
self.available_samples, priorities = parameters
self.sum_tree = SumTree(priorities)
def __init__(self, buffer_size, batch_size, seed):
self.batch_size = batch_size
self.experience = namedtuple(
"Experience",
field_names=["state", "action", "reward", "next_state", "done"])
self.seed = random.seed(seed)
self.tree = SumTree(buffer_size)
def __init__(self, capacity):
'''
Initializes PRB.
Args:
capacity: capacity of backing SumTree
'''
self.tree = SumTree(capacity)
self.capacity = capacity
def __init__(self, capacity):
# Making the tree
"""
Remember that our tree is composed of a sum tree that contains the priority scores at his leaf
And also a data array
We don't use deque because it means that at each timestep our experiences change index by one.
We prefer to use a simple array and to overwrite when the memory is full.
"""
self.tree = SumTree(capacity)
def __init__(self, capacity, e=0.01, a=0.6):
"""
:param capacity: The maximum number of samples that can be stored
:param e: Ensures that no sample has 0 priority
:param a:
"""
self.capacity = capacity
self.e = e
self.a = a
self.tree = SumTree(capacity)
def __init__(self, batch_size, buffer_size, seed):
self.seed = random.seed(seed)
self.epsilon = 0.01 # small amount to avoid zero priority
self.alpha = 0.6 # [0~1] convert the importance of TD error to priority,
# it is a trade-off between using priority and totally uniformly randomness
# self.absolute_error_upper = 1.0 # clipped abs error (abs error is the absolute value of TD error)
self.beta = 0.4 # importance-sampling, from initial value increasing to 1
self.beta_increment_per_sampling = 0.001
self.batch_size = batch_size
self.buffer_size = buffer_size
self.sumtree = SumTree(buffer_size)
def __init__(self, capacity, alpha):
super(PrioritizedReplayMemory, self).__init__(capacity)
self.max_priority = 1.0
self.tree_ptr = 0
self.alpha = alpha
tree_capacity = 1
while tree_capacity <= self.capacity:
tree_capacity *= 2
self.sum_tree = SumTree(tree_capacity)
self.min_tree = MinTree(tree_capacity)
def __init__(self, args):
if args.load_path is None:
self.model_params = ModelParameters(args)
self.training_params = TrainingParameters(args)
self.model, self.target_model = create_models(self.model_params)
else:
self.model_params = args.model_params
self.training_params = args.training_params
self.model, self.target_model = create_models(self.model_params, args.load_path+'/weights')
self.replay = SumTree()
# self.replay = []
self.replay_index = None
def __init__(self,
capacity,
epsilon=0.01,
alpha=0.6,
beta=0.4,
beta_increment=0.001):
self.epsilon = epsilon
self.alpha = alpha
self.beta = beta
self.beta_increment = beta_increment
self.capacity = capacity
self.tree = SumTree(self.capacity)
def __init__(self, max_size, window_size, input_shape):
# set default sumtree
self.tree = SumTree(max_size)
self._max_size = max_size
# dimension for how to store state and next state
self._window_size = window_size
self._WIDTH = input_shape[0]
self._HEIGHT = input_shape[1]
# hyperparmeters for priority probability
self.e = 0.01
self.a = 0.6
def __init__(self, buffer_size, with_per = False):
""" Initialization
"""
if(with_per):
# Prioritized Experience Replay
self.alpha = 0.5
self.epsilon = 0.01
self.buffer = SumTree(buffer_size)
else:
# Standard Buffer
self.buffer = deque()
self.count = 0
self.with_per = with_per
self.buffer_size = buffer_size
class Memory: # stored as ( s, a, r, s_ ) in SumTree
def __init__(self, capacity):
self.tree = SumTree(capacity)
self.max_p = 1
self.e = 0.0
self.a = 0.6
def _getPriority(self, error):
return (error + self.e)**self.a
def length(self):
return self.tree.write
def add(self, sample, error):
p = self._getPriority(error)
self.tree.add(p, sample)
def add_p(self, p, sample):
self.tree.add(p, sample)
def sample(self, n):
batch = []
idx_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(data)
idx_batch.append(idx)
return batch, idx_batch
def update(self, idx, error):
p = self._getPriority(error)
if p > self.max_p:
self.max_p = p
self.tree.update(idx, p)
def update_batch(self, idx_batch, error_batch):
p_batch = self._getPriority(error_batch)
if np.max(p_batch) > self.max_p:
self.max_p = np.max(p_batch)
self.tree.update_batch(idx_batch, p_batch)
def testUpdating(self):
tree = SumTree(4)
tree.push(0, 1)
tree.push(1, 2)
tree.push(2, 3)
tree.push(3, 4)
tree.push(0, 5)
self.assertEqual(tree._data[0], 2 + 3 + 4 + 5)
class PrioritizedReplayMemory:
e = 0.01
a = 0.6
def __init__(self, capacity):
self.tree = SumTree(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)
def isFull(self):
return self.tree.isFull()
class Memory(object): # stored as ( s, a, r, s_ ) in SumTree
"""
This SumTree code is modified version and the original code is from:
https://github.com/jaara/AI-blog/blob/master/Seaquest-DDQN-PER.py
"""
epsilon = 0.01 # small amount to avoid zero priority
alpha = 0.6 # [0~1] convert the importance of TD error to priority
beta = 0.4 # importance-sampling, from initial value increasing to 1
beta_increment_per_sampling = 0.001
abs_err_upper = 1. # clipped abs error
def __init__(self, capacity):
self.tree = SumTree(capacity)
def store(self, transition):
max_p = np.max(self.tree.tree[-self.tree.capacity:])
if max_p == 0:
max_p = self.abs_err_upper
self.tree.add(max_p, transition) # set the max p for new p
def sample(self, n):
# b_idx, b_memory, ISWeights = np.empty((n,), dtype=np.int32), np.empty((n, self.tree.data[0].size)), np.empty((n, 1))
b_idx, b_memory, ISWeights = deque(), deque(), deque()
pri_seg = self.tree.total_p / n # priority segment
self.beta = np.min([1., self.beta + self.beta_increment_per_sampling]) # max = 1
max_prob = np.max(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, 0] = np.power(prob/max_prob, -self.beta)
ISWeights.append(np.power(prob/max_prob, -self.beta))
# b_idx[i], b_memory[i, :] = idx, data
b_idx.append(idx)
b_memory.append(data)
return np.array(list(b_idx)), np.array(list(b_memory)), np.reshape(np.array(list(ISWeights)), (n, 1))
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 testRemoving(self):
tree = SumTree(2)
tree.push(0, 1)
tree.push(1, 2)
tree.pop(0)
self.assertEqual(tree._data[0], 2)
class PrioritizedER:
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 (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)
if data == 0:
p = priorities[-1]
data = batch[-1]
idx = idxs[-1]
print(
'WARNING: transition value was 0, replaced it with the previous sampled transition'
)
priorities.append(p)
batch.append(data)
idxs.append(idx)
sampling_probabilities = (priorities / self.tree.total()) + 10e-5
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 Memory: # stored as ( s, a, r, s_ ) in SumTree
e = 0.01
a = 0.6
def __init__(self, capacity):
self.tree = SumTree(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)
def __init__(self, max_capacity: int, batch_size: int,
state_size: tuple, action_size: tuple):
self.state_size = state_size
self.action_size = action_size
self.buffer_size = max_capacity
# buffer to store (s,a,r,s',d) tuples
self.buffer = [(np.zeros(shape=self.state_size),
np.zeros(shape=self.action_size),
0.0,
np.zeros(shape=self.state_size),
0.0) for i in range(self.buffer_size)]
# Initially all priorities are set to zero
self.sum_tree = SumTree([0 for i in range(self.buffer_size)])
self.curr_write_idx = 0
self.available_samples = 0
self.beta = 0.4 # Importance sampling factor
self.alpha = 0.6 # priority factor
self.min_priority = 0.01
self.batch_size = batch_size
def testSampling(self):
tree = SumTree(4)
tree.push(0, 1)
tree.push(1, 2)
tree.push(2, 3)
tree.push(3, 4)
num_samples = int(1e5)
res = tree.sample(num_samples)
self.assertTrue(np.abs(np.sum(res == 3) / num_samples - 0.4) < 0.1)
self.assertTrue(np.abs(np.sum(res == 2) / num_samples - 0.3) < 0.1)
self.assertTrue(np.abs(np.sum(res == 1) / num_samples - 0.2) < 0.1)
class Memory:
"""
Stores transitions as (s, a, r, s_, done) tuples using a SumTree.
Each sample is assigned a priority which affects retrieval
"""
def __init__(self, capacity, e=0.01, a=0.6):
"""
:param capacity: The maximum number of samples that can be stored
:param e: Ensures that no sample has 0 priority
:param a:
"""
self.capacity = capacity
self.e = e
self.a = a
self.tree = SumTree(capacity)
def _getPriority(self, error):
return (error + self.e)**self.a
def add(self, error, sample):
"""
Adds a new sample to the buffer
:param error: The error associated with the sample
:param sample: The sample to add
"""
p = self._getPriority(error)
self.tree.add(p, sample)
def sample(self, n):
"""
Returns n samples from the buffer
:param n: The number of samples to return
"""
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 PrioritizedMemory(Memory):
def __init__(self,
capacity,
epsilon=0.01,
alpha=0.6,
beta=0.4,
beta_increment=0.001):
self.epsilon = epsilon
self.alpha = alpha
self.beta = beta
self.beta_increment = beta_increment
self.capacity = capacity
self.tree = SumTree(self.capacity)
def _compute_priority(self, loss):
return (np.abs(loss) + self.epsilon)**self.alpha
def push(self, *args):
priority = self.tree.max()
priority = 1 if priority <= 0 else priority
self.tree.add(priority, Transition(*args))
def sample(self, batch_size):
batch = []
indices = []
weights = np.empty(batch_size, dtype='float32')
self.beta += self.beta_increment
beta = np.minimum(1., self.beta)
total = self.tree.total()
for i, r in enumerate(np.random.uniform(0, total, (batch_size, ))):
index, priority, data = self.tree.get(r)
batch.append(data)
indices.append(index)
weights[i] = (self.capacity * priority / total)**(-beta)
return batch, indices, weights / weights.max()
def update(self, index, loss):
priority = self._compute_priority(loss)
self.tree.update(index, priority)
def __len__(self):
return self.tree.n_entries
class Memory:
# Constants
e = 0.01
a = 0.0 #0.6
# Initialize memory
def __init__(self, capacity):
self.tree = SumTree(capacity)
self.capacity = capacity
self.len = 0
# Calculate error priority
def getPriority(self, error):
return (error + self.e)**self.a
# Add sample to the memory
def add(self, error, sample):
p = self.getPriority(error)
self.tree.add(p, sample)
self.len = min(self.len + 1, self.capacity)
# Generate 'n' random samples from the memory
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
# Number of current samples in memory
def numberSamples(self):
return self.len
# Update priority of error
def update(self, idx, error):
p = self.getPriority(error)
self.tree.update(idx, p)
class PERMemory(ReplayMemory):
epsilon = 0.0001
alpha = 0.6
def __init__(self, CAPACITY):
super(PERMemory, self).__init__(CAPACITY)
self.tree = SumTree(CAPACITY)
self.size = 0
# Proportional prioritizationによるpriorityの計算
def _getPriority(self, td_error):
return (td_error + self.epsilon)**self.alpha
def push(self, state, action, state_next, reward):
"""state, action, state_next, rewardをメモリに保存します"""
self.size += 1
priority = self.tree.max()
if priority <= 0:
priority = 1
self.tree.add(priority, Transition(state, action, state_next, reward))
def sample(self, batch_size):
data_list = []
indexes = []
for rand in np.random.uniform(0, self.tree.total(), batch_size):
(idx, _, data) = self.tree.get(rand)
data_list.append(data)
indexes.append(idx)
return data_list, indexes
def update(self, idx, td_error):
priority = self._getPriority(td_error)
self.tree.update(idx, priority)
def __len__(self):
return self.size
class Replay_Memory:
def __init__(self):
self.memory_len = 10000
self.memory_bias = .01
self.memory_pow = .6
self.tree = SumTree(self.memory_len)
def add(self, error, sample):
priority = (error + self.memory_bias)**self.memory_pow
self.tree.add(priority, sample)
def sample(self, batch_size):
"""
Get a sample batch of the replay memory
Returns:
batch: a batch with one sample from each segment of the memory
"""
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, _, 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 + self.memory_bias)**self.memory_pow
self.tree.update(idx, priority)
class DuelingModel:
def __init__(self, args):
if args.load_path is None:
self.model_params = ModelParameters(args)
self.training_params = TrainingParameters(args)
self.model, self.target_model = create_models(self.model_params)
else:
self.model_params = args.model_params
self.training_params = args.training_params
self.model, self.target_model = create_models(self.model_params, args.load_path+'/weights')
self.replay = SumTree()
# self.replay = []
self.replay_index = None
def replay_batch(self):
batch_size = self.training_params.batch_size
ixs = []
if self.training_params.prioritize:
sum_all = self.replay.tree[0].sum
for i in range(batch_size):
sample_value = sum_all / batch_size * (i + np.random.rand())
ixs.append(self.replay.sample(sample_value))
holders = [self.replay.tree[ix].pointer for ix in ixs]
return holders, ixs
else:
for i in range(batch_size):
ixs.append(self.replay.sample_random())
return [self.replay.tree[ix].pointer for ix in ixs], ixs
# ixs = np.random.choice(len(self.replay), size=batch_size)
# return [self.replay[ix] for ix in ixs]
def train_on_batch(self):
batch, batch_ixs = self.replay_batch()
pre_sample = np.array([h.s_t for h in batch])
post_sample = np.array([h.s_tp1 for h in batch])
qpre = self.model.predict(pre_sample)
qpost = self.target_model.predict(post_sample)
q1 = np.zeros(qpre.shape[0])
q2 = np.zeros_like(q1)
for i in xrange(len(batch)):
q1[i] = qpre[i, batch[i].a_t] # XXX: max instead?
if batch[i].last:
qpre[i, batch[i].a_t] = batch[i].r_t
else:
qpre[i, batch[i].a_t] = batch[i].r_t + self.training_params.gamma * np.amax(qpost[i])
q2[i] = qpre[i, batch[i].a_t]
delta = q1 - q2
w = self.get_p_weights(delta, batch, batch_ixs)
self.model.train_on_batch(pre_sample, qpre, sample_weight=w)
def add_to_replay(self, h, training_started=False):
if np.isnan(h.r_t):
print 'nan in reward (!)'
return
if training_started:
h.delta = self.replay.tree[0].sum/self.training_params.batch_size # make sure it'll be sampled once
else:
h.delta = 1.
if len(self.replay.tree) >= self.training_params.replay_size:
if self.replay_index is None:
self.replay_index = self.replay.last_ixs()
ix = self.replay_index.next()
self.replay.tree[ix].pointer = h
self.replay.tree[ix].sum = h.delta
self.replay.update(ix)
else:
self.replay.add_node(h)
def get_p_weights(self, delta, batch, batch_ixs):
"""Output weights for prioritizing bias compensation"""
# if self.is_rnn:
# s = s[:, -1]
if self.training_params.prioritize:
p_total = self.replay.tree[0].sum
p = np.array([h.delta for h in batch]) / p_total
w = 1. / p
w /= np.max(w)
for ix, h, d in zip(batch_ixs, batch, delta):
h.delta = np.nan_to_num(np.abs(d)) # catch nans
self.replay.update(ix)
else:
w = np.ones(len(batch))
return w
def get_delta(self, batch):
pre_sample = np.array([h.s_t for h in batch])
post_sample = np.array([h.s_tp1 for h in batch])
qpre = self.model.predict(pre_sample)
qpost = self.target_model.predict(post_sample)
q1 = np.zeros(qpre.shape[0])
q2 = np.zeros_like(q1)
for i in xrange(len(batch)):
q1[i] = qpre[i, batch[i].a_t] # XXX: max instead?
if batch[i].last:
qpre[i, batch[i].a_t] = batch[i].r_t
else:
qpre[i, batch[i].a_t] = batch[i].r_t + self.training_params.gamma * np.amax(qpost[i])
q2[i] = qpre[i, batch[i].a_t]
delta = q1 - q2
return delta
def heap_update(self):
"""Every n steps, recalculate deltas in the sumtree"""
print 'SumTree pre-update:', self.replay.tree[0].sum
last_ixs = self.replay.last_ixs(True)
while True:
if len(last_ixs) == 0:
break
if len(last_ixs) < 10000:
ixs = last_ixs
last_ixs = []
else:
ixs = last_ixs[:10000]
last_ixs = last_ixs[10000:]
batch = [self.replay.tree[ix].pointer for ix in ixs]
delta = self.get_delta(batch)
self.get_p_weights(delta, batch, ixs)
print 'SumTree post-update:', self.replay.tree[0].sum
print 'SumTree updated'
def save(self, save_path, folder_name):
if not os.path.exists(save_path + folder_name):
os.makedirs(save_path + folder_name)
self.target_model.save_weights(save_path + folder_name + '/weights', overwrite=True)
info = self.model_params, self.training_params
d_file = open(save_path + folder_name + '/model_params', 'wr')
cPickle.dump(info, d_file)
d_file.close()
class Memory:
def __init__(self, max_capacity: int, batch_size: int,
state_size: tuple, action_size: tuple):
self.state_size = state_size
self.action_size = action_size
self.buffer_size = max_capacity
# buffer to store (s,a,r,s',d) tuples
self.buffer = [(np.zeros(shape=self.state_size),
np.zeros(shape=self.action_size),
0.0,
np.zeros(shape=self.state_size),
0.0) for i in range(self.buffer_size)]
# Initially all priorities are set to zero
self.sum_tree = SumTree([0 for i in range(self.buffer_size)])
self.curr_write_idx = 0
self.available_samples = 0
self.beta = 0.4 # Importance sampling factor
self.alpha = 0.6 # priority factor
self.min_priority = 0.01
self.batch_size = batch_size
def __len__(self):
return self.available_samples
def record(self, experience: tuple, priority: float):
# add the experience to the buffer
self.buffer[self.curr_write_idx] = experience
# update the priority of this experience in the sum tree
self.update(self.curr_write_idx, priority)
self.curr_write_idx += 1
if self.curr_write_idx >= self.buffer_size:
self.curr_write_idx = 0
if self.available_samples < self.buffer_size:
self.available_samples += 1 # max value = self.buffer_size
def adjust_priority(self, priority: float):
return np.power(priority + self.min_priority, self.alpha)
def update(self, idx: int, priority: float):
self.sum_tree.update(self.sum_tree.leaf_nodes[idx],
self.adjust_priority(priority))
def sample(self):
sampled_idxs = []
is_weights = [] # importance sampling weights
sample_no = 0
while sample_no < self.batch_size:
sample_val = np.random.uniform(0, self.sum_tree.root_node.value)
sample_node = self.sum_tree.retrieve(sample_val, self.sum_tree.root_node)
# check if this is a valid idx
if sample_node.idx < self.available_samples:
sampled_idxs.append(sample_node.idx)
p = sample_node.value / (self.sum_tree.root_node.value + 1e-3) # avoid singularity
is_weights.append(self.available_samples * p) # give equal weights
sample_no += 1
# while loop ends here
# apply beta factor and normalise so that maximum is_weight < 1
is_weights = np.array(is_weights)
is_weights = np.power(is_weights, -self.beta)
is_weights = is_weights / np.max(is_weights) # normalize to (0, 1)
# load states and next_states
state_batch = []
action_batch = []
reward_batch = []
next_state_batch = []
done_batch = []
for i in range(len(sampled_idxs)):
state_batch.append(self.buffer[sampled_idxs[i]][0])
action_batch.append(self.buffer[sampled_idxs[i]][1])
reward_batch.append(self.buffer[sampled_idxs[i]][2])
next_state_batch.append(self.buffer[sampled_idxs[i]][3])
done_batch.append(self.buffer[sampled_idxs[i]][4])
return state_batch, action_batch, reward_batch,\
next_state_batch, done_batch, sampled_idxs, is_weights
def save_priorities_txt(self, filename):
priorities = self.sum_tree.get_priorities()
with open(filename, 'w') as file:
for i in range(self.buffer_size):
file.write('{}\t{}\n'.format(i, priorities[i]))
def save_data(self, buffer_filename):
# get priorities
priorities = self.sum_tree.get_priorities()
parameters = (
self.buffer,
self.curr_write_idx,
self.available_samples,
priorities
)
with open(buffer_filename, 'wb') as file:
pickle.dump(parameters, file)
def load_data(self, buffer_filename):
with open(buffer_filename, 'rb') as file:
parameters = pickle.load(file)
self.buffer, self.curr_write_idx, \
self.available_samples, priorities = parameters
self.sum_tree = SumTree(priorities)
def __init__(self, capacity):
self.tree = SumTree(capacity)
self.capacity = capacity
self.len = 0
def __init__(self, capacity):
self.tree = SumTree(capacity)
self.max_p = 1
self.e = 0.0
self.a = 0.6
def clear(self):
""" Clear buffer / Sum Tree
"""
if(self.with_per): self.buffer = SumTree(buffer_size)
else: self.buffer = deque()
self.count = 0
class MemoryBuffer(object):
""" Memory Buffer Helper class for Experience Replay
using a double-ended queue or a Sum Tree (for PER)
"""
def __init__(self, buffer_size, with_per = False):
""" Initialization
"""
if(with_per):
# Prioritized Experience Replay
self.alpha = 0.5
self.epsilon = 0.01
self.buffer = SumTree(buffer_size)
else:
# Standard Buffer
self.buffer = deque()
self.count = 0
self.with_per = with_per
self.buffer_size = buffer_size
def memorize(self, state, action, reward, done, new_state, achieved_goal, goal, error=None):
""" Save an experience to memory, optionally with its TD-Error
"""
experience = (state, action, reward, done, new_state, achieved_goal, goal, error)
if(self.with_per):
priority = self.priority(error[0])
self.buffer.add(priority, experience)
self.count += 1
else:
# Check if buffer is already full
if self.count < self.buffer_size:
self.buffer.append(experience)
self.count += 1
else:
self.buffer.popleft()
self.buffer.append(experience)
def priority(self, error):
""" Compute an experience priority, as per Schaul et al.
"""
return (error + self.epsilon) ** self.alpha
def size(self):
""" Current Buffer Occupation
"""
return self.count
def sample_batch(self, batch_size):
""" Sample a batch, optionally with (PER)
"""
batch = []
# Sample using prorities
if(self.with_per):
T = self.buffer.total() // batch_size
for i in range(batch_size):
a, b = T * i, T * (i + 1)
s = random.uniform(a, b)
idx, error, data = self.buffer.get(s)
batch.append((*data, idx))
idx = np.array([i[7] for i in batch])
# Sample randomly from Buffer
elif self.count < batch_size:
idx = None
batch = random.sample(self.buffer, self.count)
else:
idx = None
batch = random.sample(self.buffer, batch_size)
# Return a batch of experience
s_batch = np.array([i[0] for i in batch])
a_batch = np.array([i[1] for i in batch])
r_batch = np.array([i[2] for i in batch])
d_batch = np.array([i[3] for i in batch])
new_s_batch = np.array([i[4] for i in batch])
ag_batch = np.array([i[5] for i in batch])
g_batch = np.array([i[6] for i in batch])
return s_batch, a_batch, r_batch, d_batch, new_s_batch, ag_batch, g_batch, idx
def update(self, idx, new_error):
""" Update priority for idx (PER)
"""
self.buffer.update(idx, self.priority(new_error))
def clear(self):
""" Clear buffer / Sum Tree
"""
if(self.with_per): self.buffer = SumTree(buffer_size)
else: self.buffer = deque()
self.count = 0
def __init__(self, capacity):
self.tree = SumTree(capacity)
from sumtree import SumTree
tree = SumTree(memory_size=10)
p = 1
for i in range(p):
tree.add(10000, (1, 1, 1, 1, 1))
print("tree",tree.tree)
print("transition",tree.transitions)
