Source code for ray.rllib.policy.sample_batch

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"

[docs]@PublicAPI class SampleBatch: """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" # Extra action fetches keys. ACTION_DIST_INPUTS = "action_dist_inputs" ACTION_PROB = "action_prob" ACTION_LOGP = "action_logp" # 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).""" = dict(*args, **kwargs) lengths = [] for k, v in assert isinstance(k, str), self lengths.append(len(v))[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",, lengths) 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)
[docs] @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})
[docs] @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
[docs] @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
[docs] @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]
[docs] @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 =["eps_id"][0] offset = 0 for i in range(self.count): next_eps_id =["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
[docs] @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})
@PublicAPI def keys(self): return @PublicAPI def items(self): return @PublicAPI def get(self, key): return @PublicAPI def __getitem__(self, key): return[key] @PublicAPI def __setitem__(self, key, item):[key] = item @DeveloperAPI def compress(self, bulk=False, columns=frozenset(["obs", "new_obs"])): for key in columns: if key in if bulk:[key] = pack([key]) else:[key] = np.array( [pack(o) for o in[key]]) @DeveloperAPI def decompress_if_needed(self, columns=frozenset(["obs", "new_obs"])): for key in columns: if key in arr =[key] if is_compressed(arr):[key] = unpack(arr) elif len(arr) > 0 and is_compressed(arr[0]):[key] = np.array( [unpack(o) for o in[key]]) return self def __str__(self): return "SampleBatch({})".format(str( def __repr__(self): return "SampleBatch({})".format(str( def __iter__(self): return def __contains__(self, x): return x in
@PublicAPI class MultiAgentBatch: """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) return self 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)