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from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import six
import collections
import numpy as np
from ray.rllib.utils.annotations import PublicAPI, DeveloperAPI
from ray.rllib.utils.compression import pack, unpack, is_compressed
from ray.rllib.utils.memory import concat_aligned
# Default policy id for single agent environments
DEFAULT_POLICY_ID = "default_policy"
@PublicAPI
class SampleBatch(object):
"""Wrapper around a dictionary with string keys and array-like values.
For example, {"obs": [1, 2, 3], "reward": [0, -1, 1]} is a batch of three
samples, each with an "obs" and "reward" attribute.
"""
# Outputs from interacting with the environment
CUR_OBS = "obs"
NEXT_OBS = "new_obs"
ACTIONS = "actions"
REWARDS = "rewards"
PREV_ACTIONS = "prev_actions"
PREV_REWARDS = "prev_rewards"
DONES = "dones"
INFOS = "infos"
# Uniquely identifies an episode
EPS_ID = "eps_id"
# Uniquely identifies a sample batch. This is important to distinguish RNN
# sequences from the same episode when multiple sample batches are
# concatenated (fusing sequences across batches can be unsafe).
UNROLL_ID = "unroll_id"
# Uniquely identifies an agent within an episode
AGENT_INDEX = "agent_index"
# Value function predictions emitted by the behaviour policy
VF_PREDS = "vf_preds"
@PublicAPI
def __init__(self, *args, **kwargs):
"""Constructs a sample batch (same params as dict constructor)."""
self.data = dict(*args, **kwargs)
lengths = []
for k, v in self.data.copy().items():
assert isinstance(k, six.string_types), self
lengths.append(len(v))
self.data[k] = np.array(v, copy=False)
if not lengths:
raise ValueError("Empty sample batch")
assert len(set(lengths)) == 1, "data columns must be same length"
self.count = lengths[0]
@staticmethod
@PublicAPI
def concat_samples(samples):
if isinstance(samples[0], MultiAgentBatch):
return MultiAgentBatch.concat_samples(samples)
out = {}
samples = [s for s in samples if s.count > 0]
for k in samples[0].keys():
out[k] = concat_aligned([s[k] for s in samples])
return SampleBatch(out)
@PublicAPI
def concat(self, other):
"""Returns a new SampleBatch with each data column concatenated.
Examples:
>>> b1 = SampleBatch({"a": [1, 2]})
>>> b2 = SampleBatch({"a": [3, 4, 5]})
>>> print(b1.concat(b2))
{"a": [1, 2, 3, 4, 5]}
"""
assert self.keys() == other.keys(), "must have same columns"
out = {}
for k in self.keys():
out[k] = concat_aligned([self[k], other[k]])
return SampleBatch(out)
@PublicAPI
def copy(self):
return SampleBatch(
{k: np.array(v, copy=True)
for (k, v) in self.data.items()})
@PublicAPI
def rows(self):
"""Returns an iterator over data rows, i.e. dicts with column values.
Examples:
>>> batch = SampleBatch({"a": [1, 2, 3], "b": [4, 5, 6]})
>>> for row in batch.rows():
print(row)
{"a": 1, "b": 4}
{"a": 2, "b": 5}
{"a": 3, "b": 6}
"""
for i in range(self.count):
row = {}
for k in self.keys():
row[k] = self[k][i]
yield row
@PublicAPI
def columns(self, keys):
"""Returns a list of just the specified columns.
Examples:
>>> batch = SampleBatch({"a": [1], "b": [2], "c": [3]})
>>> print(batch.columns(["a", "b"]))
[[1], [2]]
"""
out = []
for k in keys:
out.append(self[k])
return out
@PublicAPI
def shuffle(self):
"""Shuffles the rows of this batch in-place."""
permutation = np.random.permutation(self.count)
for key, val in self.items():
self[key] = val[permutation]
@PublicAPI
def split_by_episode(self):
"""Splits this batch's data by `eps_id`.
Returns:
list of SampleBatch, one per distinct episode.
"""
slices = []
cur_eps_id = self.data["eps_id"][0]
offset = 0
for i in range(self.count):
next_eps_id = self.data["eps_id"][i]
if next_eps_id != cur_eps_id:
slices.append(self.slice(offset, i))
offset = i
cur_eps_id = next_eps_id
slices.append(self.slice(offset, self.count))
for s in slices:
slen = len(set(s["eps_id"]))
assert slen == 1, (s, slen)
assert sum(s.count for s in slices) == self.count, (slices, self.count)
return slices
@PublicAPI
def slice(self, start, end):
"""Returns a slice of the row data of this batch.
Arguments:
start (int): Starting index.
end (int): Ending index.
Returns:
SampleBatch which has a slice of this batch's data.
"""
return SampleBatch({k: v[start:end] for k, v in self.data.items()})
@PublicAPI
def keys(self):
return self.data.keys()
@PublicAPI
def items(self):
return self.data.items()
@PublicAPI
def __getitem__(self, key):
return self.data[key]
@PublicAPI
def __setitem__(self, key, item):
self.data[key] = item
@DeveloperAPI
def compress(self, bulk=False, columns=frozenset(["obs", "new_obs"])):
for key in columns:
if key in self.data:
if bulk:
self.data[key] = pack(self.data[key])
else:
self.data[key] = np.array(
[pack(o) for o in self.data[key]])
@DeveloperAPI
def decompress_if_needed(self, columns=frozenset(["obs", "new_obs"])):
for key in columns:
if key in self.data:
arr = self.data[key]
if is_compressed(arr):
self.data[key] = unpack(arr)
elif len(arr) > 0 and is_compressed(arr[0]):
self.data[key] = np.array(
[unpack(o) for o in self.data[key]])
def __str__(self):
return "SampleBatch({})".format(str(self.data))
def __repr__(self):
return "SampleBatch({})".format(str(self.data))
def __iter__(self):
return self.data.__iter__()
def __contains__(self, x):
return x in self.data
@PublicAPI
class MultiAgentBatch(object):
"""A batch of experiences from multiple policies in the environment.
Attributes:
policy_batches (dict): Mapping from policy id to a normal SampleBatch
of experiences. Note that these batches may be of different length.
count (int): The number of timesteps in the environment this batch
contains. This will be less than the number of transitions this
batch contains across all policies in total.
"""
@PublicAPI
def __init__(self, policy_batches, count):
self.policy_batches = policy_batches
self.count = count
@staticmethod
@PublicAPI
def wrap_as_needed(batches, count):
if len(batches) == 1 and DEFAULT_POLICY_ID in batches:
return batches[DEFAULT_POLICY_ID]
return MultiAgentBatch(batches, count)
@staticmethod
@PublicAPI
def concat_samples(samples):
policy_batches = collections.defaultdict(list)
total_count = 0
for s in samples:
assert isinstance(s, MultiAgentBatch)
for policy_id, batch in s.policy_batches.items():
policy_batches[policy_id].append(batch)
total_count += s.count
out = {}
for policy_id, batches in policy_batches.items():
out[policy_id] = SampleBatch.concat_samples(batches)
return MultiAgentBatch(out, total_count)
@PublicAPI
def copy(self):
return MultiAgentBatch(
{k: v.copy()
for (k, v) in self.policy_batches.items()}, self.count)
@PublicAPI
def total(self):
ct = 0
for batch in self.policy_batches.values():
ct += batch.count
return ct
@DeveloperAPI
def compress(self, bulk=False, columns=frozenset(["obs", "new_obs"])):
for batch in self.policy_batches.values():
batch.compress(bulk=bulk, columns=columns)
@DeveloperAPI
def decompress_if_needed(self, columns=frozenset(["obs", "new_obs"])):
for batch in self.policy_batches.values():
batch.decompress_if_needed(columns)
def __str__(self):
return "MultiAgentBatch({}, count={})".format(
str(self.policy_batches), self.count)
def __repr__(self):
return "MultiAgentBatch({}, count={})".format(
str(self.policy_batches), self.count)
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