Source code for ray.rllib.policy.sample_batch

import collections
import numpy as np
import sys
import itertools
import tree  # pip install dm_tree
from typing import Dict, Iterator, List, Optional, Set, Union

from ray.util import log_once
from ray.rllib.utils.annotations import Deprecated, DeveloperAPI, \
    PublicAPI
from ray.rllib.utils.compression import pack, unpack, is_compressed
from ray.rllib.utils.deprecation import deprecation_warning
from ray.rllib.utils.framework import try_import_tf, try_import_torch
from ray.rllib.utils.memory import concat_aligned
from ray.rllib.utils.typing import PolicyID, TensorType

tf1, tf, tfv = try_import_tf()
torch, _ = try_import_torch()

# Default policy id for single agent environments
DEFAULT_POLICY_ID = "default_policy"


[docs]@PublicAPI class SampleBatch(dict): """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 OBS = "obs" CUR_OBS = "obs" NEXT_OBS = "new_obs" ACTIONS = "actions" REWARDS = "rewards" PREV_ACTIONS = "prev_actions" PREV_REWARDS = "prev_rewards" DONES = "dones" INFOS = "infos" SEQ_LENS = "seq_lens" T = "t" # 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" # An env ID (e.g. the index for a vectorized sub-env). ENV_ID = "env_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). Note: All *args and those **kwargs not listed below will be passed as-is to the parent dict constructor. Keyword Args: _time_major (Optinal[bool]): Whether data in this sample batch is time-major. This is False by default and only relevant if the data contains sequences. _max_seq_len (Optional[bool]): The max sequence chunk length if the data contains sequences. _zero_padded (Optional[bool]): Whether the data in this batch contains sequences AND these sequences are right-zero-padded according to the `_max_seq_len` setting. _is_training (Optional[bool]): Whether this batch is used for training. If False, batch may be used for e.g. action computations (inference). """ # Possible seq_lens (TxB or BxT) setup. self.time_major = kwargs.pop("_time_major", None) # Maximum seq len value. self.max_seq_len = kwargs.pop("_max_seq_len", None) # Is alredy right-zero-padded? self.zero_padded = kwargs.pop("_zero_padded", False) # Whether this batch is used for training (vs inference). self.is_training = kwargs.pop("_is_training", None) # Call super constructor. This will make the actual data accessible # by column name (str) via e.g. self["some-col"]. dict.__init__(self, *args, **kwargs) self.accessed_keys = set() self.added_keys = set() self.deleted_keys = set() self.intercepted_values = {} self.get_interceptor = None # Clear out None seq-lens. seq_lens_ = self.get(SampleBatch.SEQ_LENS) if seq_lens_ is None or \ (isinstance(seq_lens_, list) and len(seq_lens_) == 0): self.pop(SampleBatch.SEQ_LENS, None) # Numpyfy seq_lens if list. elif isinstance(seq_lens_, list): self[SampleBatch.SEQ_LENS] = seq_lens_ = \ np.array(seq_lens_, dtype=np.int32) if self.max_seq_len is None and seq_lens_ is not None and \ not (tf and tf.is_tensor(seq_lens_)) and \ len(seq_lens_) > 0: self.max_seq_len = max(seq_lens_) if self.is_training is None: self.is_training = self.pop("is_training", False) lengths = [] copy_ = {k: v for k, v in self.items() if k != SampleBatch.SEQ_LENS} for k, v in copy_.items(): assert isinstance(k, str), self # TODO: Drop support for lists as values. # Convert lists of int|float into numpy arrays make sure all data # has same length. if isinstance(v, list): self[k] = np.array(v) # Try to infer the "length" of the SampleBatch by finding the first # value that is actually a ndarray/tensor. This would fail if # all values are nested dicts/tuples of more complex underlying # structures. len_ = len(v) if isinstance( v, (list, np.ndarray)) or (torch and torch.is_tensor(v)) else None if len_: lengths.append(len_) if self.get(SampleBatch.SEQ_LENS) is not None and \ not (tf and tf.is_tensor(self[SampleBatch.SEQ_LENS])) and \ len(self[SampleBatch.SEQ_LENS]) > 0: self.count = sum(self[SampleBatch.SEQ_LENS]) else: self.count = lengths[0] if lengths else 0 # A convenience map for slicing this batch into sub-batches along # the time axis. This helps reduce repeated iterations through the # batch's seq_lens array to find good slicing points. Built lazily # when needed. self._slice_map = [] @PublicAPI def __len__(self): """Returns the amount of samples in the sample batch.""" return self.count
[docs] @staticmethod @PublicAPI def concat_samples( samples: Union[List["SampleBatch"], List["MultiAgentBatch"]], ) -> Union["SampleBatch", "MultiAgentBatch"]: """Concatenates n SampleBatches or MultiAgentBatches. Args: samples (Union[List[SampleBatch], List[MultiAgentBatch]]): List of SampleBatches or MultiAgentBatches to be concatenated. Returns: Union[SampleBatch, MultiAgentBatch]: A new (concatenated) SampleBatch or MultiAgentBatch. Examples: >>> b1 = SampleBatch({"a": np.array([1, 2]), ... "b": np.array([10, 11])}) >>> b2 = SampleBatch({"a": np.array([3]), ... "b": np.array([12])}) >>> print(SampleBatch.concat_samples([b1, b2])) {"a": np.array([1, 2, 3]), "b": np.array([10, 11, 12])} """ if isinstance(samples[0], MultiAgentBatch): return MultiAgentBatch.concat_samples(samples) concatd_seq_lens = [] concat_samples = [] zero_padded = samples[0].zero_padded max_seq_len = samples[0].max_seq_len time_major = samples[0].time_major for s in samples: if s.count > 0: assert s.zero_padded == zero_padded assert s.time_major == time_major if zero_padded: assert s.max_seq_len == max_seq_len concat_samples.append(s) if s.get(SampleBatch.SEQ_LENS) is not None: concatd_seq_lens.extend(s[SampleBatch.SEQ_LENS]) # If we don't have any samples (0 or only empty SampleBatches), # return an empty SampleBatch here. if len(concat_samples) == 0: return SampleBatch() # Collect the concat'd data. concatd_data = {} def concat_key(*values): return concat_aligned(values, time_major) try: for k in concat_samples[0].keys(): if k == "infos": concatd_data[k] = concat_aligned( [s[k] for s in concat_samples], time_major=time_major) else: concatd_data[k] = tree.map_structure( concat_key, *[c[k] for c in concat_samples]) except Exception: raise ValueError(f"Cannot concat data under key '{k}', b/c " "sub-structures under that key don't match. " f"`samples`={samples}") # Return a new (concat'd) SampleBatch. return SampleBatch( concatd_data, seq_lens=concatd_seq_lens, _time_major=time_major, _zero_padded=zero_padded, _max_seq_len=max_seq_len, )
[docs] @PublicAPI def concat(self, other: "SampleBatch") -> "SampleBatch": """Concatenates `other` to this one and returns a new SampleBatch. Args: other (SampleBatch): The other SampleBatch object to concat to this one. Returns: SampleBatch: The new SampleBatch, resulting from concating `other` to `self`. Examples: >>> b1 = SampleBatch({"a": np.array([1, 2])}) >>> b2 = SampleBatch({"a": np.array([3, 4, 5])}) >>> print(b1.concat(b2)) {"a": np.array([1, 2, 3, 4, 5])} """ return self.concat_samples([self, other])
[docs] @PublicAPI def copy(self, shallow: bool = False) -> "SampleBatch": """Creates a deep or shallow copy of this SampleBatch and returns it. Args: shallow (bool): Whether the copying should be done shallowly. Returns: SampleBatch: A deep or shallow copy of this SampleBatch object. """ copy_ = {k: v for k, v in self.items()} data = tree.map_structure( lambda v: (np.array(v, copy=not shallow) if isinstance(v, np.ndarray) else v), copy_, ) copy_ = SampleBatch(data) copy_.set_get_interceptor(self.get_interceptor) return copy_
[docs] @PublicAPI def rows(self) -> Iterator[Dict[str, TensorType]]: """Returns an iterator over data rows, i.e. dicts with column values. Note that if `seq_lens` is set in self, we set it to [1] in the rows. Yields: Dict[str, TensorType]: The column values of the row in this iteration. Examples: >>> batch = SampleBatch({ ... "a": [1, 2, 3], ... "b": [4, 5, 6], ... "seq_lens": [1, 2] ... }) >>> for row in batch.rows(): print(row) {"a": 1, "b": 4, "seq_lens": [1]} {"a": 2, "b": 5, "seq_lens": [1]} {"a": 3, "b": 6, "seq_lens": [1]} """ # Do we add seq_lens=[1] to each row? seq_lens = None if self.get( SampleBatch.SEQ_LENS) is None else np.array([1]) self_as_dict = {k: v for k, v in self.items()} for i in range(self.count): yield tree.map_structure_with_path( lambda p, v: v[i] if p[0] != self.SEQ_LENS else seq_lens, self_as_dict, )
[docs] @PublicAPI def columns(self, keys: List[str]) -> List[any]: """Returns a list of the batch-data in the specified columns. Args: keys (List[str]): List of column names fo which to return the data. Returns: List[any]: The list of data items ordered by the order of column names in `keys`. Examples: >>> batch = SampleBatch({"a": [1], "b": [2], "c": [3]}) >>> print(batch.columns(["a", "b"])) [[1], [2]] """ # TODO: (sven) Make this work for nested data as well. out = [] for k in keys: out.append(self[k]) return out
[docs] @PublicAPI def shuffle(self) -> None: """Shuffles the rows of this batch in-place. Returns: SampleBatch: This very (now shuffled) SampleBatch. Raises: ValueError: If self[SampleBatch.SEQ_LENS] is defined. Examples: >>> batch = SampleBatch({"a": [1, 2, 3, 4]}) >>> print(batch.shuffle()) {"a": [4, 1, 3, 2]} """ # Shuffling the data when we have `seq_lens` defined is probably # a bad idea! if self.get(SampleBatch.SEQ_LENS) is not None: raise ValueError( "SampleBatch.shuffle not possible when your data has " "`seq_lens` defined!") # Get a permutation over the single items once and use the same # permutation for all the data (otherwise, data would become # meaningless). permutation = np.random.permutation(self.count) def _permutate_in_place(path, value): curr = self for i, p in enumerate(path): if i == len(path) - 1: curr[p] = value[permutation] curr = curr[p] tree.map_structure_with_path(_permutate_in_place, self) return self
[docs] @PublicAPI def split_by_episode(self) -> List["SampleBatch"]: """Splits by `eps_id` column and returns list of new batches. Returns: List[SampleBatch]: List of batches, one per distinct episode. Raises: KeyError: If the `eps_id` AND `dones` columns are not present. Examples: >>> batch = SampleBatch({"a": [1, 2, 3], "eps_id": [0, 0, 1]}) >>> print(batch.split_by_episode()) [{"a": [1, 2], "eps_id": [0, 0]}, {"a": [3], "eps_id": [1]}] """ # No eps_id in data -> Make sure there are no "dones" in the middle # and add eps_id automatically. if SampleBatch.EPS_ID not in self: # TODO: (sven) Shouldn't we rather split by DONEs then and not # add fake eps-ids (0s) at all? if SampleBatch.DONES in self: assert not any(self[SampleBatch.DONES][:-1]) self[SampleBatch.EPS_ID] = np.repeat(0, self.count) return [self] # Produce a new slice whenever we find a new episode ID. slices = [] cur_eps_id = self[SampleBatch.EPS_ID][0] offset = 0 for i in range(self.count): next_eps_id = self[SampleBatch.EPS_ID][i] if next_eps_id != cur_eps_id: slices.append(self[offset:i]) offset = i cur_eps_id = next_eps_id # Add final slice. slices.append(self[offset:self.count]) # TODO: (sven) Are these checks necessary? Should be all ok according # to above logic. for s in slices: slen = len(set(s[SampleBatch.EPS_ID])) assert slen == 1, (s, slen) assert sum(s.count for s in slices) == self.count, (slices, self.count) return slices
@Deprecated(new="SampleBatch[start:stop]", error=False) def slice(self, start: int, end: int, state_start=None, state_end=None) -> "SampleBatch": """Returns a slice of the row data of this batch (w/o copying). Args: start (int): Starting index. If < 0, will left-zero-pad. end (int): Ending index. Returns: SampleBatch: A new SampleBatch, which has a slice of this batch's data. """ if self.get(SampleBatch.SEQ_LENS) is not None and \ len(self[SampleBatch.SEQ_LENS]) > 0: if start < 0: data = { k: np.concatenate([ np.zeros( shape=(-start, ) + v.shape[1:], dtype=v.dtype), v[0:end] ]) for k, v in self.items() if k != SampleBatch.SEQ_LENS and not k.startswith("state_in_") } else: data = { k: v[start:end] for k, v in self.items() if k != SampleBatch.SEQ_LENS and not k.startswith("state_in_") } if state_start is not None: assert state_end is not None state_idx = 0 state_key = "state_in_{}".format(state_idx) while state_key in self: data[state_key] = self[state_key][state_start:state_end] state_idx += 1 state_key = "state_in_{}".format(state_idx) seq_lens = list( self[SampleBatch.SEQ_LENS][state_start:state_end]) # Adjust seq_lens if necessary. data_len = len(data[next(iter(data))]) if sum(seq_lens) != data_len: assert sum(seq_lens) > data_len seq_lens[-1] = data_len - sum(seq_lens[:-1]) else: # Fix state_in_x data. count = 0 state_start = None seq_lens = None for i, seq_len in enumerate(self[SampleBatch.SEQ_LENS]): count += seq_len if count >= end: state_idx = 0 state_key = "state_in_{}".format(state_idx) if state_start is None: state_start = i while state_key in self: data[state_key] = self[state_key][state_start:i + 1] state_idx += 1 state_key = "state_in_{}".format(state_idx) seq_lens = list( self[SampleBatch.SEQ_LENS][state_start:i]) + [ seq_len - (count - end) ] if start < 0: seq_lens[0] += -start diff = sum(seq_lens) - (end - start) if diff > 0: seq_lens[0] -= diff assert sum(seq_lens) == (end - start) break elif state_start is None and count > start: state_start = i return SampleBatch( data, seq_lens=seq_lens, _time_major=self.time_major, ) else: return SampleBatch( tree.map_structure(lambda value: value[start:end], self), _is_training=self.is_training, _time_major=self.time_major, )
[docs] @PublicAPI def timeslices(self, size: Optional[int] = None, num_slices: Optional[int] = None, k: Optional[int] = None) -> List["SampleBatch"]: """Returns SampleBatches, each one representing a k-slice of this one. Will start from timestep 0 and produce slices of size=k. Args: size (Optional[int]): The size (in timesteps) of each returned SampleBatch. num_slices (Optional[int]): The number of slices to produce. k (int): Obsoleted: Use size or num_slices instead! The size (in timesteps) of each returned SampleBatch. Returns: List[SampleBatch]: The list of `num_slices` (new) SampleBatches or n (new) SampleBatches each one of size `size`. """ if size is None and num_slices is None: deprecation_warning("k", "size or num_slices") assert k is not None size = k if size is None: assert isinstance(num_slices, int) slices = [] left = len(self) start = 0 while left: len_ = left // (num_slices - len(slices)) stop = start + len_ slices.append(self[start:stop]) left -= len_ start = stop return slices else: assert isinstance(size, int) slices = [] left = len(self) start = 0 while left: stop = start + size slices.append(self[start:stop]) left -= size start = stop return slices
@Deprecated(new="SampleBatch.right_zero_pad", error=False) def zero_pad(self, max_seq_len, exclude_states=True): return self.right_zero_pad(max_seq_len, exclude_states)
[docs] def right_zero_pad(self, max_seq_len: int, exclude_states: bool = True): """Right (adding zeros at end) zero-pads this SampleBatch in-place. This will set the `self.zero_padded` flag to True and `self.max_seq_len` to the given `max_seq_len` value. Args: max_len (int): The max (total) length to zero pad to. exclude_states (bool): If True, also right-zero-pad all `state_in_x` data. If False, leave `state_in_x` keys as-is. Returns: SampleBatch: This very (now right-zero-padded) SampleBatch. Raises: ValueError: If self[SampleBatch.SEQ_LENS] is None (not defined). Examples: >>> batch = SampleBatch({"a": [1, 2, 3], "seq_lens": [1, 2]}) >>> print(batch.right_zero_pad(max_seq_len=4)) {"a": [1, 0, 0, 0, 2, 3, 0, 0], "seq_lens": [1, 2]} >>> batch = SampleBatch({"a": [1, 2, 3], ... "state_in_0": [1.0, 3.0], ... "seq_lens": [1, 2]}) >>> print(batch.right_zero_pad(max_seq_len=5)) {"a": [1, 0, 0, 0, 0, 2, 3, 0, 0, 0], "state_in_0": [1.0, 3.0], # <- all state-ins remain as-is "seq_lens": [1, 2]} """ seq_lens = self.get(SampleBatch.SEQ_LENS) if seq_lens is None: raise ValueError( "Cannot right-zero-pad SampleBatch if no `seq_lens` field " "present! SampleBatch={self}") length = len(seq_lens) * max_seq_len def _zero_pad_in_place(path, value): # Skip "state_in_..." columns and "seq_lens". if (exclude_states is True and path[0].startswith("state_in_")) \ or path[0] == SampleBatch.SEQ_LENS: return # Generate zero-filled primer of len=max_seq_len. if value.dtype == np.object or value.dtype.type is np.str_: f_pad = [None] * length else: # Make sure type doesn't change. f_pad = np.zeros( (length, ) + np.shape(value)[1:], dtype=value.dtype) # Fill primer with data. f_pad_base = f_base = 0 for len_ in self[SampleBatch.SEQ_LENS]: f_pad[f_pad_base:f_pad_base + len_] = value[f_base:f_base + len_] f_pad_base += max_seq_len f_base += len_ assert f_base == len(value), value # Update our data in-place. curr = self for i, p in enumerate(path): if i == len(path) - 1: curr[p] = f_pad curr = curr[p] self_as_dict = {k: v for k, v in self.items()} tree.map_structure_with_path(_zero_pad_in_place, self_as_dict) # Set flags to indicate, we are now zero-padded (and to what extend). self.zero_padded = True self.max_seq_len = max_seq_len return self
# Experimental method.
[docs] def to_device(self, device, framework="torch"): """TODO: transfer batch to given device as framework tensor.""" if framework == "torch": assert torch is not None for k, v in self.items(): if isinstance(v, np.ndarray) and v.dtype != np.object: self[k] = torch.from_numpy(v).to(device) else: raise NotImplementedError return self
[docs] @PublicAPI def size_bytes(self) -> int: """Returns sum over number of bytes of all data buffers. For numpy arrays, we use `.nbytes`. For all other value types, we use sys.getsizeof(...). Returns: int: The overall size in bytes of the data buffer (all columns). """ return sum( v.nbytes if isinstance(v, np.ndarray) else sys.getsizeof(v) for v in tree.flatten(self))
[docs] def get(self, key, default=None): try: return self.__getitem__(key) except KeyError: return default
@PublicAPI def __getitem__(self, key: Union[str, slice]) -> TensorType: """Returns one column (by key) from the data or a sliced new batch. Args: key (Union[str, slice]): The key (column name) to return or a slice object for slicing this SampleBatch. Returns: TensorType: The data under the given key or a sliced version of this batch. """ if isinstance(key, slice): return self._slice(key) if not hasattr(self, key) and key in self: self.accessed_keys.add(key) # Backward compatibility for when "input-dicts" were used. if key == "is_training": if log_once("SampleBatch['is_training']"): deprecation_warning( old="SampleBatch['is_training']", new="SampleBatch.is_training", error=False) return self.is_training value = dict.__getitem__(self, key) if self.get_interceptor is not None: if key not in self.intercepted_values: self.intercepted_values[key] = self.get_interceptor(value) value = self.intercepted_values[key] return value @PublicAPI def __setitem__(self, key, item) -> None: """Inserts (overrides) an entire column (by key) in the data buffer. Args: key (str): The column name to set a value for. item (TensorType): The data to insert. """ # Defend against creating SampleBatch via pickle (no property # `added_keys` and first item is already set). if not hasattr(self, "added_keys"): dict.__setitem__(self, key, item) return if key not in self: self.added_keys.add(key) dict.__setitem__(self, key, item) if key in self.intercepted_values: self.intercepted_values[key] = item @PublicAPI def __delitem__(self, key): self.deleted_keys.add(key) dict.__delitem__(self, key)
[docs] @DeveloperAPI def compress(self, bulk: bool = False, columns: Set[str] = frozenset(["obs", "new_obs"])) -> None: """Compresses the data buffers (by 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]): The columns to compress. Default: Only compress the obs and new_obs columns. Returns: SampleBatch: This very (now compressed) SampleBatch. """ def _compress_in_place(path, value): if path[0] not in columns: return curr = self for i, p in enumerate(path): if i == len(path) - 1: if bulk: curr[p] = pack(value) else: curr[p] = np.array([pack(o) for o in value]) curr = curr[p] tree.map_structure_with_path(_compress_in_place, self) return self
[docs] @DeveloperAPI def decompress_if_needed(self, columns: Set[str] = frozenset( ["obs", "new_obs"])) -> "SampleBatch": """Decompresses data buffers (per column if not compressed) in place. Args: columns (Set[str]): The columns to decompress. Default: Only decompress the obs and new_obs columns. Returns: SampleBatch: This very (now uncompressed) SampleBatch. """ def _decompress_in_place(path, value): if path[0] not in columns: return curr = self for p in path[:-1]: curr = curr[p] # Bulk compressed. if is_compressed(value): curr[path[-1]] = unpack(value) # Non bulk compressed. elif len(value) > 0 and is_compressed(value[0]): curr[path[-1]] = np.array([unpack(o) for o in value]) tree.map_structure_with_path(_decompress_in_place, self) return self
@DeveloperAPI def set_get_interceptor(self, fn): # If get-interceptor changes, must erase old intercepted values. if fn is not self.get_interceptor: self.intercepted_values = {} self.get_interceptor = fn def __repr__(self): keys = list(self.keys()) if self.get(SampleBatch.SEQ_LENS) is None: return f"SampleBatch({self.count}: {keys})" else: keys.remove(SampleBatch.SEQ_LENS) return f"SampleBatch({self.count} " \ f"(seqs={len(self['seq_lens'])}): {keys})" def _slice(self, slice_: slice): """Helper method to handle SampleBatch slicing using a slice object. The returned SampleBatch uses the same underlying data object as `self`, so changing the slice will also change `self`. Note that only zero or positive bounds are allowed for both start and stop values. The slice step must be 1 (or None, which is the same). Args: slice_ (slice): The python slice object to slice by. Returns: SampleBatch: A new SampleBatch, however "linking" into the same data (sliced) as self. """ start = slice_.start or 0 stop = slice_.stop or len(self) # If stop goes beyond the length of this batch -> Make it go till the # end only (including last item). # Analogous to `l = [0, 1, 2]; l[:100] -> [0, 1, 2];`. if stop > len(self): stop = len(self) assert start >= 0 and stop >= 0 and slice_.step in [1, None] if self.get(SampleBatch.SEQ_LENS) is not None and \ len(self[SampleBatch.SEQ_LENS]) > 0: # Build our slice-map, if not done already. if not self._slice_map: sum_ = 0 for i, l in enumerate(self[SampleBatch.SEQ_LENS]): for _ in range(l): self._slice_map.append((i, sum_)) sum_ += l # In case `stop` points to the very end (lengths of this # batch), return the last sequence (the -1 here makes sure we # never go beyond it; would result in an index error below). self._slice_map.append((len(self[SampleBatch.SEQ_LENS]), sum_)) start_seq_len, start = self._slice_map[start] stop_seq_len, stop = self._slice_map[stop] if self.zero_padded: start = start_seq_len * self.max_seq_len stop = stop_seq_len * self.max_seq_len def map_(path, value): if path[0] != SampleBatch.SEQ_LENS and not path[0].startswith( "state_in_"): return value[start:stop] else: return value[start_seq_len:stop_seq_len] data = tree.map_structure_with_path(map_, self) return SampleBatch( data, _is_training=self.is_training, _time_major=self.time_major, _zero_padded=self.zero_padded, _max_seq_len=self.max_seq_len if self.zero_padded else None, ) else: data = tree.map_structure(lambda value: value[start:stop], self) return SampleBatch( data, _is_training=self.is_training, _time_major=self.time_major, ) @Deprecated(error=False) def _get_slice_indices(self, slice_size): data_slices = [] data_slices_states = [] if self.get(SampleBatch.SEQ_LENS) is not None and len( self[SampleBatch.SEQ_LENS]) > 0: assert np.all(self[SampleBatch.SEQ_LENS] < slice_size), \ "ERROR: `slice_size` must be larger than the max. seq-len " \ "in the batch!" start_pos = 0 current_slize_size = 0 actual_slice_idx = 0 start_idx = 0 idx = 0 while idx < len(self[SampleBatch.SEQ_LENS]): seq_len = self[SampleBatch.SEQ_LENS][idx] current_slize_size += seq_len actual_slice_idx += seq_len if not self.zero_padded else \ self.max_seq_len # Complete minibatch -> Append to data_slices. if current_slize_size >= slice_size: end_idx = idx + 1 # We are not zero-padded yet; all sequences are # back-to-back. if not self.zero_padded: data_slices.append((start_pos, start_pos + slice_size)) start_pos += slice_size if current_slize_size > slice_size: overhead = current_slize_size - slice_size start_pos -= (seq_len - overhead) idx -= 1 # We are already zero-padded: Cut in chunks of max_seq_len. else: data_slices.append((start_pos, actual_slice_idx)) start_pos = actual_slice_idx data_slices_states.append((start_idx, end_idx)) current_slize_size = 0 start_idx = idx + 1 idx += 1 else: i = 0 while i < self.count: data_slices.append((i, i + slice_size)) i += slice_size return data_slices, data_slices_states # TODO: deprecate @property def data(self): deprecation_warning( old="SampleBatch.data[..]", new="SampleBatch[..]", error=True) return self # TODO: (sven) Experimental method.
[docs] def get_single_step_input_dict(self, view_requirements, index="last"): """Creates single ts SampleBatch at given index from `self`. For usage as input-dict for model calls. Args: sample_batch (SampleBatch): A single-trajectory SampleBatch object to generate the compute_actions input dict from. index (Union[int, str]): An integer index value indicating the position in the trajectory for which to generate the compute_actions input dict. Set to "last" to generate the dict at the very end of the trajectory (e.g. for value estimation). Note that "last" is different from -1, as "last" will use the final NEXT_OBS as observation input. Returns: SampleBatch: The (single-timestep) input dict for ModelV2 calls. """ last_mappings = { SampleBatch.OBS: SampleBatch.NEXT_OBS, SampleBatch.PREV_ACTIONS: SampleBatch.ACTIONS, SampleBatch.PREV_REWARDS: SampleBatch.REWARDS, } input_dict = {} for view_col, view_req in view_requirements.items(): # Create batches of size 1 (single-agent input-dict). data_col = view_req.data_col or view_col if index == "last": data_col = last_mappings.get(data_col, data_col) # Range needed. if view_req.shift_from is not None: data = self[view_col][-1] # Batch repeat value > 1: We have single frames in the # batch at each timestep. if view_req.batch_repeat_value > 1: traj_len = len(self[data_col]) missing_at_end = traj_len % view_req.batch_repeat_value obs_shift = -1 if data_col in [ SampleBatch.OBS, SampleBatch.NEXT_OBS ] else 0 from_ = view_req.shift_from + obs_shift to_ = view_req.shift_to + obs_shift + 1 if to_ == 0: to_ = None input_dict[view_col] = np.array([ np.concatenate( [self[data_col][-missing_at_end:], data])[from_:to_] ]) # Batch repeat value = 1: We already have framestacks # at each timestep. else: input_dict[view_col] = data[None] # Single index. else: input_dict[view_col] = tree.map_structure( lambda v: v[-1:], # keep as array (w/ 1 element) self[data_col], ) else: # Index range. if isinstance(index, tuple): data = self[data_col][index[0]:index[1] + 1 if index[1] != -1 else None] input_dict[view_col] = np.array([data]) # Single index. else: input_dict[view_col] = self[data_col][ index:index + 1 if index != -1 else None] return SampleBatch(input_dict, seq_lens=np.array([1], dtype=np.int32))
[docs]@PublicAPI class MultiAgentBatch: """A batch of experiences from multiple agents in the environment. Attributes: policy_batches (Dict[PolicyID, SampleBatch]): Mapping from policy ids to SampleBatches of experiences. count (int): The number of env steps in this batch. """ @PublicAPI def __init__(self, policy_batches: Dict[PolicyID, SampleBatch], env_steps: int): """Initialize a MultiAgentBatch object. Args: policy_batches (Dict[PolicyID, SampleBatch]): Mapping from policy ids to SampleBatches of experiences. env_steps (int): The number of environment steps in the environment this batch contains. This will be less than the number of transitions this batch contains across all policies in total. """ for v in policy_batches.values(): assert isinstance(v, SampleBatch) self.policy_batches = policy_batches # Called "count" for uniformity with SampleBatch. # Prefer to access this via the `env_steps()` method when possible # for clarity. self.count = env_steps
[docs] @PublicAPI def env_steps(self) -> int: """The number of env steps (there are >= 1 agent steps per env step). Returns: int: The number of environment steps contained in this batch. """ return self.count
[docs] @PublicAPI def agent_steps(self) -> int: """The number of agent steps (there are >= 1 agent steps per env step). Returns: int: The number of agent steps total in this batch. """ ct = 0 for batch in self.policy_batches.values(): ct += batch.count return ct
[docs] @PublicAPI def timeslices(self, k: int) -> List["MultiAgentBatch"]: """Returns k-step batches holding data for each agent at those steps. For examples, suppose we have agent1 observations [a1t1, a1t2, a1t3], for agent2, [a2t1, a2t3], and for agent3, [a3t3] only. Calling timeslices(1) would return three MultiAgentBatches containing [a1t1, a2t1], [a1t2], and [a1t3, a2t3, a3t3]. Calling timeslices(2) would return two MultiAgentBatches containing [a1t1, a1t2, a2t1], and [a1t3, a2t3, a3t3]. This method is used to implement "lockstep" replay mode. Note that this method does not guarantee each batch contains only data from a single unroll. Batches might contain data from multiple different envs. """ from ray.rllib.evaluation.sample_batch_builder import \ SampleBatchBuilder # Build a sorted set of (eps_id, t, policy_id, data...) steps = [] for policy_id, batch in self.policy_batches.items(): for row in batch.rows(): steps.append((row[SampleBatch.EPS_ID], row[SampleBatch.T], row[SampleBatch.AGENT_INDEX], policy_id, row)) steps.sort() finished_slices = [] cur_slice = collections.defaultdict(SampleBatchBuilder) cur_slice_size = 0 def finish_slice(): nonlocal cur_slice_size assert cur_slice_size > 0 batch = MultiAgentBatch( {k: v.build_and_reset() for k, v in cur_slice.items()}, cur_slice_size) cur_slice_size = 0 finished_slices.append(batch) # For each unique env timestep. for _, group in itertools.groupby(steps, lambda x: x[:2]): # Accumulate into the current slice. for _, _, _, policy_id, row in group: cur_slice[policy_id].add_values(**row) cur_slice_size += 1 # Slice has reached target number of env steps. if cur_slice_size >= k: finish_slice() assert cur_slice_size == 0 if cur_slice_size > 0: finish_slice() assert len(finished_slices) > 0, finished_slices return finished_slices
[docs] @staticmethod @PublicAPI def wrap_as_needed( policy_batches: Dict[PolicyID, SampleBatch], env_steps: int) -> Union[SampleBatch, "MultiAgentBatch"]: """Returns SampleBatch or MultiAgentBatch, depending on given policies. Args: policy_batches (Dict[PolicyID, SampleBatch]): Mapping from policy ids to SampleBatch. env_steps (int): Number of env steps in the batch. Returns: Union[SampleBatch, MultiAgentBatch]: The single default policy's SampleBatch or a MultiAgentBatch (more than one policy). """ if len(policy_batches) == 1 and DEFAULT_POLICY_ID in policy_batches: return policy_batches[DEFAULT_POLICY_ID] return MultiAgentBatch( policy_batches=policy_batches, env_steps=env_steps)
[docs] @staticmethod @PublicAPI def concat_samples(samples: List["MultiAgentBatch"]) -> "MultiAgentBatch": """Concatenates a list of MultiAgentBatches into a new MultiAgentBatch. Args: samples (List[MultiAgentBatch]): List of MultiagentBatch objects to concatenate. Returns: MultiAgentBatch: A new MultiAgentBatch consisting of the concatenated inputs. """ policy_batches = collections.defaultdict(list) env_steps = 0 for s in samples: if not isinstance(s, MultiAgentBatch): raise ValueError( "`MultiAgentBatch.concat_samples()` can only concat " "MultiAgentBatch types, not {}!".format(type(s).__name__)) for key, batch in s.policy_batches.items(): policy_batches[key].append(batch) env_steps += s.env_steps() out = {} for key, batches in policy_batches.items(): out[key] = SampleBatch.concat_samples(batches) return MultiAgentBatch(out, env_steps)
[docs] @PublicAPI def copy(self) -> "MultiAgentBatch": """Deep-copies self into a new MultiAgentBatch. Returns: MultiAgentBatch: The copy of self with deep-copied data. """ return MultiAgentBatch( {k: v.copy() for (k, v) in self.policy_batches.items()}, self.count)
[docs] @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())
[docs] @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)
[docs] @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)