mirror of
https://github.com/vale981/ray
synced 2025-03-06 02:21:39 -05:00
582 lines
19 KiB
Python
582 lines
19 KiB
Python
import collections
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import numpy as np
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import sys
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import itertools
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from typing import Any, Dict, Iterable, List, Optional, Set, Union
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from ray.rllib.utils.annotations import PublicAPI, DeveloperAPI
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from ray.rllib.utils.compression import pack, unpack, is_compressed
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from ray.rllib.utils.memory import concat_aligned
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from ray.rllib.utils.deprecation import deprecation_warning
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from ray.rllib.utils.typing import TensorType
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# Default policy id for single agent environments
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DEFAULT_POLICY_ID = "default_policy"
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# TODO(ekl) reuse the other id def once we fix imports
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PolicyID = Any
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@PublicAPI
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class SampleBatch:
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"""Wrapper around a dictionary with string keys and array-like values.
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For example, {"obs": [1, 2, 3], "reward": [0, -1, 1]} is a batch of three
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samples, each with an "obs" and "reward" attribute.
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"""
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# Outputs from interacting with the environment
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OBS = "obs"
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CUR_OBS = "obs"
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NEXT_OBS = "new_obs"
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ACTIONS = "actions"
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REWARDS = "rewards"
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PREV_ACTIONS = "prev_actions"
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PREV_REWARDS = "prev_rewards"
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DONES = "dones"
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INFOS = "infos"
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# Extra action fetches keys.
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ACTION_DIST_INPUTS = "action_dist_inputs"
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ACTION_PROB = "action_prob"
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ACTION_LOGP = "action_logp"
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# Uniquely identifies an episode.
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EPS_ID = "eps_id"
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# Uniquely identifies a sample batch. This is important to distinguish RNN
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# sequences from the same episode when multiple sample batches are
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# concatenated (fusing sequences across batches can be unsafe).
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UNROLL_ID = "unroll_id"
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# Uniquely identifies an agent within an episode.
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AGENT_INDEX = "agent_index"
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# Value function predictions emitted by the behaviour policy.
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VF_PREDS = "vf_preds"
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@PublicAPI
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def __init__(self, *args, **kwargs):
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"""Constructs a sample batch (same params as dict constructor)."""
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self._initial_inputs = kwargs.pop("_initial_inputs", {})
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self.data = dict(*args, **kwargs)
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lengths = []
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for k, v in self.data.copy().items():
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assert isinstance(k, str), self
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lengths.append(len(v))
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self.data[k] = np.array(v, copy=False)
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if not lengths:
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raise ValueError("Empty sample batch")
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assert len(set(lengths)) == 1, ("data columns must be same length",
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self.data, lengths)
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self.count = lengths[0]
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@staticmethod
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@PublicAPI
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def concat_samples(samples: List[Dict[str, TensorType]]) -> \
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Union["SampleBatch", "MultiAgentBatch"]:
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"""Concatenates n data dicts or MultiAgentBatches.
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Args:
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samples (List[Dict[TensorType]]]): List of dicts of data (numpy).
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Returns:
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Union[SampleBatch, MultiAgentBatch]: A new (compressed)
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SampleBatch or MultiAgentBatch.
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"""
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if isinstance(samples[0], MultiAgentBatch):
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return MultiAgentBatch.concat_samples(samples)
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out = {}
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samples = [s for s in samples if s.count > 0]
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for k in samples[0].keys():
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out[k] = concat_aligned([s[k] for s in samples])
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return SampleBatch(out)
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@PublicAPI
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def concat(self, other: "SampleBatch") -> "SampleBatch":
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"""Returns a new SampleBatch with each data column concatenated.
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Args:
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other (SampleBatch): The other SampleBatch object to concat to this
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one.
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Returns:
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SampleBatch: The new SampleBatch, resulting from concating `other`
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to `self`.
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Examples:
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>>> b1 = SampleBatch({"a": [1, 2]})
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>>> b2 = SampleBatch({"a": [3, 4, 5]})
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>>> print(b1.concat(b2))
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{"a": [1, 2, 3, 4, 5]}
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"""
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if self.keys() != other.keys():
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raise ValueError(
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"SampleBatches to concat must have same columns! {} vs {}".
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format(list(self.keys()), list(other.keys())))
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out = {}
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for k in self.keys():
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out[k] = concat_aligned([self[k], other[k]])
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return SampleBatch(out)
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@PublicAPI
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def copy(self) -> "SampleBatch":
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"""Creates a (deep) copy of this SampleBatch and returns it.
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Returns:
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SampleBatch: A (deep) copy of this SampleBatch object.
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"""
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return SampleBatch(
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{k: np.array(v, copy=True)
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for (k, v) in self.data.items()})
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@PublicAPI
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def rows(self) -> Dict[str, TensorType]:
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"""Returns an iterator over data rows, i.e. dicts with column values.
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Yields:
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Dict[str, TensorType]: The column values of the row in this
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iteration.
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Examples:
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>>> batch = SampleBatch({"a": [1, 2, 3], "b": [4, 5, 6]})
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>>> for row in batch.rows():
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print(row)
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{"a": 1, "b": 4}
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{"a": 2, "b": 5}
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{"a": 3, "b": 6}
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"""
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for i in range(self.count):
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row = {}
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for k in self.keys():
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row[k] = self[k][i]
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yield row
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@PublicAPI
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def columns(self, keys: List[str]) -> List[any]:
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"""Returns a list of the batch-data in the specified columns.
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Args:
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keys (List[str]): List of column names fo which to return the data.
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Returns:
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List[any]: The list of data items ordered by the order of column
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names in `keys`.
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Examples:
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>>> batch = SampleBatch({"a": [1], "b": [2], "c": [3]})
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>>> print(batch.columns(["a", "b"]))
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[[1], [2]]
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"""
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out = []
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for k in keys:
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out.append(self[k])
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return out
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@PublicAPI
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def shuffle(self) -> None:
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"""Shuffles the rows of this batch in-place."""
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permutation = np.random.permutation(self.count)
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for key, val in self.items():
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self[key] = val[permutation]
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@PublicAPI
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def split_by_episode(self) -> List["SampleBatch"]:
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"""Splits this batch's data by `eps_id`.
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Returns:
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List[SampleBatch]: List of batches, one per distinct episode.
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"""
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slices = []
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cur_eps_id = self.data["eps_id"][0]
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offset = 0
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for i in range(self.count):
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next_eps_id = self.data["eps_id"][i]
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if next_eps_id != cur_eps_id:
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slices.append(self.slice(offset, i))
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offset = i
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cur_eps_id = next_eps_id
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slices.append(self.slice(offset, self.count))
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for s in slices:
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slen = len(set(s["eps_id"]))
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assert slen == 1, (s, slen)
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assert sum(s.count for s in slices) == self.count, (slices, self.count)
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return slices
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@PublicAPI
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def slice(self, start: int, end: int) -> "SampleBatch":
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"""Returns a slice of the row data of this batch (w/o copying).
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Args:
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start (int): Starting index.
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end (int): Ending index.
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Returns:
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SampleBatch: A new SampleBatch, which has a slice of this batch's
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data.
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"""
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return SampleBatch({k: v[start:end] for k, v in self.data.items()})
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@PublicAPI
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def timeslices(self, k: int) -> List["SampleBatch"]:
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"""Returns SampleBatches, each one representing a k-slice of this one.
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Will start from timestep 0 and produce slices of size=k.
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Args:
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k (int): The size (in timesteps) of each returned SampleBatch.
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Returns:
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List[SampleBatch]: The list of (new) SampleBatches (each one of
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size k).
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"""
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out = []
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i = 0
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while i < self.count:
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out.append(self.slice(i, i + k))
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i += k
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return out
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@PublicAPI
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def keys(self) -> Iterable[str]:
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"""
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Returns:
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Iterable[str]: The keys() iterable over `self.data`.
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"""
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return self.data.keys()
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@PublicAPI
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def items(self) -> Iterable[TensorType]:
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"""
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Returns:
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Iterable[TensorType]: The values() iterable over `self.data`.
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"""
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return self.data.items()
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@PublicAPI
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def get(self, key: str) -> Optional[TensorType]:
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"""Returns one column (by key) from the data or None if key not found.
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Args:
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key (str): The key (column name) to return.
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Returns:
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Optional[TensorType]: The data under the given key. None if key
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not found in data.
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"""
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return self.data.get(key)
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@PublicAPI
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def size_bytes(self) -> int:
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"""
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Returns:
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int: The overall size in bytes of the data buffer (all columns).
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"""
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return sum(sys.getsizeof(d) for d in self.data)
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@PublicAPI
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def __getitem__(self, key: str) -> TensorType:
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"""Returns one column (by key) from the data.
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Args:
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key (str): The key (column name) to return.
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Returns:
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TensorType]: The data under the given key.
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"""
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return self.data[key]
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@PublicAPI
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def __setitem__(self, key, item) -> None:
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"""Inserts (overrides) an entire column (by key) in the data buffer.
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Args:
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key (str): The column name to set a value for.
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item (TensorType): The data to insert.
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"""
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self.data[key] = item
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@DeveloperAPI
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def compress(self,
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bulk: bool = False,
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columns: Set[str] = frozenset(["obs", "new_obs"])) -> None:
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"""Compresses the data buffers (by column) in place.
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Args:
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bulk (bool): Whether to compress across the batch dimension (0)
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as well. If False will compress n separate list items, where n
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is the batch size.
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columns (Set[str]): The columns to compress. Default: Only
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compress the obs and new_obs columns.
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"""
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for key in columns:
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if key in self.data:
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if bulk:
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self.data[key] = pack(self.data[key])
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else:
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self.data[key] = np.array(
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[pack(o) for o in self.data[key]])
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@DeveloperAPI
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def decompress_if_needed(self,
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columns: Set[str] = frozenset(
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["obs", "new_obs"])) -> "SampleBatch":
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"""Decompresses data buffers (per column if not compressed) in place.
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Args:
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columns (Set[str]): The columns to decompress. Default: Only
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decompress the obs and new_obs columns.
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Returns:
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SampleBatch: This very SampleBatch.
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"""
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for key in columns:
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if key in self.data:
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arr = self.data[key]
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if is_compressed(arr):
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self.data[key] = unpack(arr)
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elif len(arr) > 0 and is_compressed(arr[0]):
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self.data[key] = np.array(
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[unpack(o) for o in self.data[key]])
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return self
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def __str__(self):
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return "SampleBatch({})".format(str(self.data))
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def __repr__(self):
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return "SampleBatch({})".format(str(self.data))
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def __iter__(self):
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return self.data.__iter__()
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def __contains__(self, x):
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return x in self.data
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@PublicAPI
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class MultiAgentBatch:
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"""A batch of experiences from multiple agents in the environment.
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Attributes:
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policy_batches (Dict[PolicyID, SampleBatch]): Mapping from policy
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ids to SampleBatches of experiences.
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count (int): The number of env steps in this batch.
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"""
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@PublicAPI
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def __init__(self, policy_batches: Dict[PolicyID, SampleBatch],
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env_steps: int):
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"""Initialize a MultiAgentBatch object.
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Args:
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policy_batches (Dict[PolicyID, SampleBatch]): Mapping from policy
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ids to SampleBatches of experiences.
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env_steps (int): The number of timesteps in the environment this
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batch contains. This will be less than the number of
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transitions this batch contains across all policies in total.
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"""
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for v in policy_batches.values():
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assert isinstance(v, SampleBatch)
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self.policy_batches = policy_batches
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# Called count for uniformity with SampleBatch. Prefer to access this
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# via the env_steps() method when possible for clarity.
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self.count = env_steps
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@PublicAPI
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def env_steps(self) -> int:
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"""The number of env steps (there are >= 1 agent steps per env step).
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Returns:
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int: The number of environment steps contained in this batch.
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"""
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return self.count
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@PublicAPI
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def agent_steps(self) -> int:
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"""The number of agent steps (there are >= 1 agent steps per env step).
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Returns:
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int: The number of agent steps total in this batch.
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"""
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ct = 0
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for batch in self.policy_batches.values():
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ct += batch.count
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return ct
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@PublicAPI
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def timeslices(self, k: int) -> List["MultiAgentBatch"]:
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"""Returns k-step batches holding data for each agent at those steps.
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For examples, suppose we have agent1 observations [a1t1, a1t2, a1t3],
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for agent2, [a2t1, a2t3], and for agent3, [a3t3] only.
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Calling timeslices(1) would return three MultiAgentBatches containing
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[a1t1, a2t1], [a1t2], and [a1t3, a2t3, a3t3].
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Calling timeslices(2) would return two MultiAgentBatches containing
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[a1t1, a1t2, a2t1], and [a1t3, a2t3, a3t3].
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This method is used to implement "lockstep" replay mode. Note that this
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method does not guarantee each batch contains only data from a single
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unroll. Batches might contain data from multiple different envs.
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"""
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from ray.rllib.evaluation.sample_batch_builder import \
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SampleBatchBuilder
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# Build a sorted set of (eps_id, t, policy_id, data...)
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steps = []
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for policy_id, batch in self.policy_batches.items():
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for row in batch.rows():
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steps.append((row[SampleBatch.EPS_ID], row["t"],
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row["agent_index"], policy_id, row))
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steps.sort()
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finished_slices = []
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cur_slice = collections.defaultdict(SampleBatchBuilder)
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cur_slice_size = 0
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def finish_slice():
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nonlocal cur_slice_size
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assert cur_slice_size > 0
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batch = MultiAgentBatch(
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{k: v.build_and_reset()
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for k, v in cur_slice.items()}, cur_slice_size)
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cur_slice_size = 0
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finished_slices.append(batch)
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# For each unique env timestep.
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for _, group in itertools.groupby(steps, lambda x: x[:2]):
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# Accumulate into the current slice.
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for _, _, _, policy_id, row in group:
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cur_slice[policy_id].add_values(**row)
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cur_slice_size += 1
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# Slice has reached target number of env steps.
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if cur_slice_size >= k:
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finish_slice()
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assert cur_slice_size == 0
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if cur_slice_size > 0:
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finish_slice()
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assert len(finished_slices) > 0, finished_slices
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return finished_slices
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@staticmethod
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@PublicAPI
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def wrap_as_needed(
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policy_batches: Dict[PolicyID, SampleBatch],
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env_steps: int) -> Union[SampleBatch, "MultiAgentBatch"]:
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"""Returns SampleBatch or MultiAgentBatch, depending on given policies.
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Args:
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policy_batches (Dict[PolicyID, SampleBatch]): Mapping from policy
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ids to SampleBatch.
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env_steps (int): Number of env steps in the batch.
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Returns:
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Union[SampleBatch, MultiAgentBatch]: The single default policy's
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SampleBatch or a MultiAgentBatch (more than one policy).
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"""
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if len(policy_batches) == 1 and DEFAULT_POLICY_ID in policy_batches:
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return policy_batches[DEFAULT_POLICY_ID]
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return MultiAgentBatch(policy_batches, env_steps)
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@staticmethod
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@PublicAPI
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def concat_samples(samples: List["MultiAgentBatch"]) -> "MultiAgentBatch":
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"""Concatenates a list of MultiAgentBatches into a new MultiAgentBatch.
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Args:
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samples (List[MultiAgentBatch]): List of MultiagentBatch objects
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to concatenate.
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Returns:
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MultiAgentBatch: A new MultiAgentBatch consisting of the
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concatenated inputs.
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"""
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policy_batches = collections.defaultdict(list)
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env_steps = 0
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for s in samples:
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if not isinstance(s, MultiAgentBatch):
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raise ValueError(
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"`MultiAgentBatch.concat_samples()` can only concat "
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"MultiAgentBatch types, not {}!".format(type(s).__name__))
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for key, batch in s.policy_batches.items():
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policy_batches[key].append(batch)
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env_steps += s.env_steps()
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out = {}
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for key, batches in policy_batches.items():
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out[key] = SampleBatch.concat_samples(batches)
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return MultiAgentBatch(out, env_steps)
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@PublicAPI
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def copy(self) -> "MultiAgentBatch":
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"""Deep-copies self into a new MultiAgentBatch.
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Returns:
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MultiAgentBatch: The copy of self with deep-copied data.
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"""
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return MultiAgentBatch(
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{k: v.copy()
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for (k, v) in self.policy_batches.items()}, self.count)
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@PublicAPI
|
|
def size_bytes(self) -> int:
|
|
"""
|
|
Returns:
|
|
int: The overall size in bytes of all policy batches (all columns).
|
|
"""
|
|
return sum(b.size_bytes() for b in self.policy_batches.values())
|
|
|
|
@DeveloperAPI
|
|
def compress(self,
|
|
bulk: bool = False,
|
|
columns: Set[str] = frozenset(["obs", "new_obs"])) -> None:
|
|
"""Compresses each policy batch (per column) in place.
|
|
|
|
Args:
|
|
bulk (bool): Whether to compress across the batch dimension (0)
|
|
as well. If False will compress n separate list items, where n
|
|
is the batch size.
|
|
columns (Set[str]): Set of column names to compress.
|
|
"""
|
|
for batch in self.policy_batches.values():
|
|
batch.compress(bulk=bulk, columns=columns)
|
|
|
|
@DeveloperAPI
|
|
def decompress_if_needed(self,
|
|
columns: Set[str] = frozenset(
|
|
["obs", "new_obs"])) -> "MultiAgentBatch":
|
|
"""Decompresses each policy batch (per column), if already compressed.
|
|
|
|
Args:
|
|
columns (Set[str]): Set of column names to decompress.
|
|
|
|
Returns:
|
|
MultiAgentBatch: This very MultiAgentBatch.
|
|
"""
|
|
for batch in self.policy_batches.values():
|
|
batch.decompress_if_needed(columns)
|
|
return self
|
|
|
|
def __str__(self):
|
|
return "MultiAgentBatch({}, env_steps={})".format(
|
|
str(self.policy_batches), self.count)
|
|
|
|
def __repr__(self):
|
|
return "MultiAgentBatch({}, env_steps={})".format(
|
|
str(self.policy_batches), self.count)
|
|
|
|
# Deprecated.
|
|
def total(self):
|
|
deprecation_warning("batch.total()", "batch.agent_steps()")
|
|
return self.agent_steps()
|