class PrioritizedReplayBuffer(ReplayBuffer):
def __init__(self, size, alpha):
"""
Create Prioritized Replay buffer.
See Also ReplayBuffer.__init__
:param size: (int) Max number of transitions to store in the buffer. When the buffer overflows the old memories
are dropped.
:param alpha: (float) how much prioritization is used (0 - no prioritization, 1 - full prioritization)
"""
super(PrioritizedReplayBuffer, self).__init__(size)
assert alpha >= 0
self._alpha = alpha
it_capacity = 1
while it_capacity < size:
it_capacity *= 2
self._it_sum = SumSegmentTree(it_capacity)
self._it_min = MinSegmentTree(it_capacity)
self._max_priority = 1.0
def add(self, obs_t, action, reward, obs_tp1, done):
"""
add a new transition to the buffer
:param obs_t: (Any) the last observation
:param action: ([float]) the action
:param reward: (float) the reward of the transition
:param obs_tp1: (Any) the current observation
:param done: (bool) is the episode done
"""
idx = self._next_idx
super().add(obs_t, action, reward, obs_tp1, done)
self._it_sum[idx] = self._max_priority**self._alpha
self._it_min[idx] = self._max_priority**self._alpha
def _sample_proportional(self, batch_size):
res = []
for _ in range(batch_size):
# TODO(szymon): should we ensure no repeats?
mass = random.random() * self._it_sum.sum(0,
len(self._storage) - 1)
idx = self._it_sum.find_prefixsum_idx(mass)
res.append(idx)
return res
def sample(self, batch_size, beta=0):
"""
Sample a batch of experiences.
compared to ReplayBuffer.sample
it also returns importance weights and idxes
of sampled experiences.
:param batch_size: (int) How many transitions to sample.
:param beta: (float) To what degree to use importance weights (0 - no corrections, 1 - full correction)
:return:
- obs_batch: (np.ndarray) batch of observations
- act_batch: (numpy float) batch of actions executed given obs_batch
- rew_batch: (numpy float) rewards received as results of executing act_batch
- next_obs_batch: (np.ndarray) next set of observations seen after executing act_batch
- done_mask: (numpy bool) done_mask[i] = 1 if executing act_batch[i] resulted in the end of an episode
and 0 otherwise.
- weights: (numpy float) Array of shape (batch_size,) and dtype np.float32 denoting importance weight of
each sampled transition
- idxes: (numpy int) Array of shape (batch_size,) and dtype np.int32 idexes in buffer of sampled experiences
"""
assert beta > 0
idxes = self._sample_proportional(batch_size)
weights = []
p_min = self._it_min.min() / self._it_sum.sum()
max_weight = (p_min * len(self._storage))**(-beta)
for idx in idxes:
p_sample = self._it_sum[idx] / self._it_sum.sum()
weight = (p_sample * len(self._storage))**(-beta)
weights.append(weight / max_weight)
weights = np.array(weights)
encoded_sample = self._encode_sample(idxes)
return tuple(list(encoded_sample) + [weights, idxes])
def update_priorities(self, idxes, priorities):
"""
Update priorities of sampled transitions.
sets priority of transition at index idxes[i] in buffer
to priorities[i].
:param idxes: ([int]) List of idxes of sampled transitions
:param priorities: ([float]) List of updated priorities corresponding to transitions at the sampled idxes
denoted by variable `idxes`.
"""
assert len(idxes) == len(priorities)
for idx, priority in zip(idxes, priorities):
assert priority > 0
assert 0 <= idx < len(self._storage)
self._it_sum[idx] = priority**self._alpha
self._it_min[idx] = priority**self._alpha
self._max_priority = max(self._max_priority, priority)
class TwoWayPrioritizedReplayBuffer(ReplayBuffer):
def __init__(self, size, alpha):
super(PrioritizedReplayBuffer, self).__init__(size)
assert alpha >= 0
self._alpha = alpha
it_capacity = 1
while it_capacity < size:
it_capacity *= 2
self._it_sum = SumSegmentTree(it_capacity)
self._it_min = MinSegmentTree(it_capacity)
self._max_priority = 1.0
def add(self, obs_t, action, reward, obs_tp1, done):
idx = self._next_idx
super().add(obs_t, action, reward, obs_tp1, done)
self._it_sum[idx] = self._max_priority**self._alpha
self._it_min[idx] = self._max_priority**self._alpha
def extend(self, obs_t, action, reward, obs_tp1, done):
idx = self._next_idx
super().extend(obs_t, action, reward, obs_tp1, done)
while idx != self._next_idx:
self._it_sum[idx] = self._max_priority**self._alpha
self._it_min[idx] = self._max_priority**self._alpha
idx = (idx + 1) % self._maxsize
def _sample_proportional(self, batch_size):
mass = []
total = self._it_sum.sum(0, len(self._storage) - 1)
# TODO(szymon): should we ensure no repeats?
mass = np.random.random(size=batch_size) * total
idx = self._it_sum.find_prefixsum_idx(mass)
return idx
def sample(self,
batch_size: int,
beta: float = 0,
env: Optional[VecNormalize] = None):
assert beta > 0
idxes = self._sample_proportional(batch_size)
weights = []
p_min = self._it_min.min() / self._it_sum.sum()
max_weight = (p_min * len(self._storage))**(-beta)
p_sample = self._it_sum[idxes] / self._it_sum.sum()
weights = (p_sample * len(self._storage))**(-beta) / max_weight
encoded_sample = self._encode_sample(idxes, env=env)
return tuple(list(encoded_sample) + [weights, idxes])
def _sample_invproportional(self, batch_size):
mass = []
total = self._it_sum.sum(len(self._storage) - 1, 0)
# TODO(szymon): should we ensure no repeats?
mass = np.random.random(size=batch_size) * total
idx = self._it_sum.find_prefixsum_idx(mass)
return idx
def invsample(self,
batch_size: int,
beta: float = 0,
env: Optional[VecNormalize] = None):
assert beta > 0
idxes = self._sample_invproportional(batch_size)
encoded_sample = self._encode_sample(idxes, env=env)
return list(encoded_sample)
def update_priorities(self, idxes, priorities):
assert len(idxes) == len(priorities)
assert np.min(priorities) > 0
assert np.min(idxes) >= 0
assert np.max(idxes) < len(self.storage)
self._it_sum[idxes] = priorities**self._alpha
self._it_min[idxes] = priorities**self._alpha
self._max_priority = max(self._max_priority, np.max(priorities))
def test_max_interval_tree():
"""
test Segment Tree data structure
"""
tree = MinSegmentTree(4)
tree[0] = 1.0
tree[2] = 0.5
tree[3] = 3.0
assert np.isclose(tree.min(), 0.5)
assert np.isclose(tree.min(0, 2), 1.0)
assert np.isclose(tree.min(0, 3), 0.5)
assert np.isclose(tree.min(0, -1), 0.5)
assert np.isclose(tree.min(2, 4), 0.5)
assert np.isclose(tree.min(3, 4), 3.0)
tree[2] = 0.7
assert np.isclose(tree.min(), 0.7)
assert np.isclose(tree.min(0, 2), 1.0)
assert np.isclose(tree.min(0, 3), 0.7)
assert np.isclose(tree.min(0, -1), 0.7)
assert np.isclose(tree.min(2, 4), 0.7)
assert np.isclose(tree.min(3, 4), 3.0)
tree[2] = 4.0
assert np.isclose(tree.min(), 1.0)
assert np.isclose(tree.min(0, 2), 1.0)
assert np.isclose(tree.min(0, 3), 1.0)
assert np.isclose(tree.min(0, -1), 1.0)
assert np.isclose(tree.min(2, 4), 3.0)
assert np.isclose(tree.min(2, 3), 4.0)
assert np.isclose(tree.min(2, -1), 4.0)
assert np.isclose(tree.min(3, 4), 3.0)
class PrioritizedReplayBuffer(ReplayBuffer):
def __init__(self, size, alpha):
"""
Create Prioritized Replay buffer.
See Also ReplayBuffer.__init__
:param size: (int) Max number of transitions to store in the buffer. When the buffer overflows the old memories
are dropped.
:param alpha: (float) how much prioritization is used (0 - no prioritization, 1 - full prioritization)
"""
super(PrioritizedReplayBuffer, self).__init__(size)
assert alpha >= 0
self._alpha = alpha
it_capacity = 1
while it_capacity < size:
it_capacity *= 2
self._it_sum = SumSegmentTree(it_capacity)
self._it_min = MinSegmentTree(it_capacity)
self._max_priority = 1.0
def add(self, obs_t, action, reward, obs_tp1, done):
"""
add a new transition to the buffer
:param obs_t: (Any) the last observation
:param action: ([float]) the action
:param reward: (float) the reward of the transition
:param obs_tp1: (Any) the current observation
:param done: (bool) is the episode done
"""
idx = self._next_idx
super().add(obs_t, action, reward, obs_tp1, done)
self._it_sum[idx] = self._max_priority**self._alpha
self._it_min[idx] = self._max_priority**self._alpha
def extend(self, obs_t, action, reward, obs_tp1, done):
"""
add a new batch of transitions to the buffer
:param obs_t: (Union[Tuple[Union[np.ndarray, int]], np.ndarray]) the last batch of observations
:param action: (Union[Tuple[Union[np.ndarray, int]]], np.ndarray]) the batch of actions
:param reward: (Union[Tuple[float], np.ndarray]) the batch of the rewards of the transition
:param obs_tp1: (Union[Tuple[Union[np.ndarray, int]], np.ndarray]) the current batch of observations
:param done: (Union[Tuple[bool], np.ndarray]) terminal status of the batch
Note: uses the same names as .add to keep compatibility with named argument passing
but expects iterables and arrays with more than 1 dimensions
"""
idx = self._next_idx
super().extend(obs_t, action, reward, obs_tp1, done)
while idx != self._next_idx:
self._it_sum[idx] = self._max_priority**self._alpha
self._it_min[idx] = self._max_priority**self._alpha
idx = (idx + 1) % self._maxsize
def _sample_proportional(self, batch_size):
mass = []
total = self._it_sum.sum(0, len(self._storage) - 1)
# TODO(szymon): should we ensure no repeats?
mass = np.random.random(size=batch_size) * total
idx = self._it_sum.find_prefixsum_idx(mass)
return idx
def sample(self,
batch_size: int,
beta: float = 0,
env: Optional[VecNormalize] = None):
"""
Sample a batch of experiences.
compared to ReplayBuffer.sample
it also returns importance weights and idxes
of sampled experiences.
:param batch_size: (int) How many transitions to sample.
:param beta: (float) To what degree to use importance weights (0 - no corrections, 1 - full correction)
:param env: (Optional[VecNormalize]) associated gym VecEnv
to normalize the observations/rewards when sampling
:return:
- obs_batch: (np.ndarray) batch of observations
- act_batch: (numpy float) batch of actions executed given obs_batch
- rew_batch: (numpy float) rewards received as results of executing act_batch
- next_obs_batch: (np.ndarray) next set of observations seen after executing act_batch
- done_mask: (numpy bool) done_mask[i] = 1 if executing act_batch[i] resulted in the end of an episode
and 0 otherwise.
- weights: (numpy float) Array of shape (batch_size,) and dtype np.float32 denoting importance weight of
each sampled transition
- idxes: (numpy int) Array of shape (batch_size,) and dtype np.int32 idexes in buffer of sampled experiences
"""
assert beta > 0
idxes = self._sample_proportional(batch_size)
weights = []
p_min = self._it_min.min() / self._it_sum.sum()
max_weight = (p_min * len(self._storage))**(-beta)
p_sample = self._it_sum[idxes] / self._it_sum.sum()
weights = (p_sample * len(self._storage))**(-beta) / max_weight
encoded_sample = self._encode_sample(idxes, env=env)
return tuple(list(encoded_sample) + [weights, idxes])
def update_priorities(self, idxes, priorities):
"""
Update priorities of sampled transitions.
sets priority of transition at index idxes[i] in buffer
to priorities[i].
:param idxes: ([int]) List of idxes of sampled transitions
:param priorities: ([float]) List of updated priorities corresponding to transitions at the sampled idxes
denoted by variable `idxes`.
"""
assert len(idxes) == len(priorities)
assert np.min(priorities) > 0
assert np.min(idxes) >= 0
assert np.max(idxes) < len(self.storage)
self._it_sum[idxes] = priorities**self._alpha
self._it_min[idxes] = priorities**self._alpha
self._max_priority = max(self._max_priority, np.max(priorities))
class PrioritizedReplayBuffer(ReplayBuffer):
def __init__(self, size, alpha):
"""Create Prioritized Replay buffer.
Parameters
----------
size: int
Max number of transitions to store in the buffer. When the buffer
overflows the old memories are dropped.
alpha: float
how much prioritization is used
(0 - no prioritization, 1 - full prioritization)
See Also
--------
ReplayBuffer.__init__
"""
super(PrioritizedReplayBuffer, self).__init__(size)
assert alpha > 0
self._alpha = alpha
it_capacity = 1
while it_capacity < size:
it_capacity *= 2
self._it_sum = SumSegmentTree(it_capacity)
self._it_min = MinSegmentTree(it_capacity)
self._max_priority = 1.0
def add(self, *args, **kwargs):
"""See ReplayBuffer.store_effect"""
idx = self._next_idx
super().add(*args, **kwargs)
self._it_sum[idx] = self._max_priority**self._alpha
self._it_min[idx] = self._max_priority**self._alpha
def _sample_proportional(self, batch_size):
res = []
for _ in range(batch_size):
# TODO(szymon): should we ensure no repeats?
mass = random.random() * self._it_sum.sum(0,
len(self._storage) - 1)
idx = self._it_sum.find_prefixsum_idx(mass)
res.append(idx)
return res
def sample(self, batch_size, beta):
"""Sample a batch of experiences.
compared to ReplayBuffer.sample
it also returns importance weights and idxes
of sampled experiences.
Parameters
----------
batch_size: int
How many transitions to sample.
beta: float
To what degree to use importance weights
(0 - no corrections, 1 - full correction)
Returns
-------
obs_batch: np.array
batch of observations
act_batch: np.array
batch of actions executed given obs_batch
R_batch: np.array
returns received as results of executing act_batch
weights: np.array
Array of shape (batch_size,) and dtype np.float32
denoting importance weight of each sampled transition
idxes: np.array
Array of shape (batch_size,) and dtype np.int32
idexes in buffer of sampled experiences
"""
idxes = self._sample_proportional(batch_size)
if beta > 0:
weights = []
p_min = self._it_min.min() / self._it_sum.sum()
max_weight = (p_min * len(self._storage))**(-beta)
for idx in idxes:
p_sample = self._it_sum[idx] / self._it_sum.sum()
weight = (p_sample * len(self._storage))**(-beta)
weights.append(weight / max_weight)
weights = np.array(weights)
else:
weights = np.ones_like(idxes, dtype=np.float32)
encoded_sample = self._encode_sample(idxes)
return tuple(list(encoded_sample) + [weights, idxes])
def update_priorities(self, idxes, priorities):
"""Update priorities of sampled transitions.
sets priority of transition at index idxes[i] in buffer
to priorities[i].
Parameters
----------
idxes: [int]
List of idxes of sampled transitions
priorities: [float]
List of updated priorities corresponding to
transitions at the sampled idxes denoted by
variable `idxes`.
"""
assert len(idxes) == len(priorities)
for idx, priority in zip(idxes, priorities):
priority = max(priority, 1e-6)
assert priority > 0
assert 0 <= idx < len(self._storage)
self._it_sum[idx] = priority**self._alpha
self._it_min[idx] = priority**self._alpha
self._max_priority = max(self._max_priority, priority)
