import logging import platform from typing import Any, Dict, List, Optional, Callable, Union import numpy as np import random from enum import Enum # Import ray before psutil will make sure we use psutil's bundled version import ray # noqa F401 import psutil # noqa E402 from ray.util.debug import log_once from ray.rllib.policy.sample_batch import SampleBatch from ray.rllib.utils.deprecation import Deprecated from ray.rllib.utils.metrics.window_stat import WindowStat from ray.rllib.utils.typing import SampleBatchType, T from ray.util.annotations import DeveloperAPI from ray.util.iter import ParallelIteratorWorker # Constant that represents all policies in lockstep replay mode. _ALL_POLICIES = "__all__" logger = logging.getLogger(__name__) @DeveloperAPI class StorageUnit(Enum): TIMESTEPS = "timesteps" SEQUENCES = "sequences" EPISODES = "episodes" FRAGMENTS = "fragments" @DeveloperAPI def warn_replay_capacity(*, item: SampleBatchType, num_items: int) -> None: """Warn if the configured replay buffer capacity is too large.""" if log_once("replay_capacity"): item_size = item.size_bytes() psutil_mem = psutil.virtual_memory() total_gb = psutil_mem.total / 1e9 mem_size = num_items * item_size / 1e9 msg = ( "Estimated max memory usage for replay buffer is {} GB " "({} batches of size {}, {} bytes each), " "available system memory is {} GB".format( mem_size, num_items, item.count, item_size, total_gb ) ) if mem_size > total_gb: raise ValueError(msg) elif mem_size > 0.2 * total_gb: logger.warning(msg) else: logger.info(msg) @DeveloperAPI class ReplayBuffer(ParallelIteratorWorker): def __init__( self, capacity: int = 10000, storage_unit: Union[str, StorageUnit] = "timesteps", **kwargs, ): """Initializes a (FIFO) ReplayBuffer instance. Args: capacity: Max number of timesteps to store in this FIFO buffer. After reaching this number, older samples will be dropped to make space for new ones. storage_unit: Either 'timesteps', `sequences` or `episodes`. Specifies how experiences are stored. **kwargs: Forward compatibility kwargs. """ if storage_unit in ["timesteps", StorageUnit.TIMESTEPS]: self._storage_unit = StorageUnit.TIMESTEPS elif storage_unit in ["sequences", StorageUnit.SEQUENCES]: self._storage_unit = StorageUnit.SEQUENCES elif storage_unit in ["episodes", StorageUnit.EPISODES]: self._storage_unit = StorageUnit.EPISODES elif storage_unit in ["fragments", StorageUnit.FRAGMENTS]: self._storage_unit = StorageUnit.FRAGMENTS else: raise ValueError( "storage_unit must be either 'timesteps', `sequences` or `episodes` " "or `fragments`, but is {}".format(storage_unit) ) # The actual storage (list of SampleBatches or MultiAgentBatches). self._storage = [] # Caps the number of timesteps stored in this buffer if capacity <= 0: raise ValueError( "Capacity of replay buffer has to be greater than zero " "but was set to {}.".format(capacity) ) self.capacity = capacity # The next index to override in the buffer. self._next_idx = 0 # len(self._hit_count) must always be less than len(capacity) self._hit_count = np.zeros(self.capacity) # Whether we have already hit our capacity (and have therefore # started to evict older samples). self._eviction_started = False # Number of (single) timesteps that have been added to the buffer # over its lifetime. Note that each added item (batch) may contain # more than one timestep. self._num_timesteps_added = 0 self._num_timesteps_added_wrap = 0 # Number of (single) timesteps that have been sampled from the buffer # over its lifetime. self._num_timesteps_sampled = 0 self._evicted_hit_stats = WindowStat("evicted_hit", 1000) self._est_size_bytes = 0 self.batch_size = None def __len__(self) -> int: """Returns the number of items currently stored in this buffer.""" return len(self._storage) @DeveloperAPI def add(self, batch: SampleBatchType, **kwargs) -> None: """Adds a batch of experiences to this buffer. Also splits experiences into chunks of timesteps, sequences or episodes, depending on self._storage_unit. Calls self._add_single_batch. Args: batch: Batch to add to this buffer's storage. **kwargs: Forward compatibility kwargs. """ if not batch.count > 0: return warn_replay_capacity(item=batch, num_items=self.capacity / batch.count) if self._storage_unit == StorageUnit.TIMESTEPS: self._add_single_batch(batch, **kwargs) elif self._storage_unit == StorageUnit.SEQUENCES: timestep_count = 0 for seq_len in batch.get(SampleBatch.SEQ_LENS): start_seq = timestep_count end_seq = timestep_count + seq_len self._add_single_batch(batch[start_seq:end_seq], **kwargs) timestep_count = end_seq elif self._storage_unit == StorageUnit.EPISODES: for eps in batch.split_by_episode(): if ( eps.get(SampleBatch.T)[0] == 0 and eps.get(SampleBatch.DONES)[-1] == True # noqa E712 ): # Only add full episodes to the buffer self._add_single_batch(eps, **kwargs) else: if log_once("only_full_episodes"): logger.info( "This buffer uses episodes as a storage " "unit and thus allows only full episodes " "to be added to it. Some samples may be " "dropped." ) elif self._storage_unit == StorageUnit.FRAGMENTS: self._add_single_batch(batch, **kwargs) @DeveloperAPI def _add_single_batch(self, item: SampleBatchType, **kwargs) -> None: """Add a SampleBatch of experiences to self._storage. An item consists of either one or more timesteps, a sequence or an episode. Differs from add() in that it does not consider the storage unit or type of batch and simply stores it. Args: item: The batch to be added. **kwargs: Forward compatibility kwargs. """ self._num_timesteps_added += item.count self._num_timesteps_added_wrap += item.count if self._next_idx >= len(self._storage): self._storage.append(item) self._est_size_bytes += item.size_bytes() else: item_to_be_removed = self._storage[self._next_idx] self._est_size_bytes -= item_to_be_removed.size_bytes() self._storage[self._next_idx] = item self._est_size_bytes += item.size_bytes() # Eviction of older samples has already started (buffer is "full"). if self._eviction_started: self._evicted_hit_stats.push(self._hit_count[self._next_idx]) self._hit_count[self._next_idx] = 0 # Wrap around storage as a circular buffer once we hit capacity. if self._num_timesteps_added_wrap >= self.capacity: self._eviction_started = True self._num_timesteps_added_wrap = 0 self._next_idx = 0 else: self._next_idx += 1 @DeveloperAPI def sample(self, num_items: int, **kwargs) -> Optional[SampleBatchType]: """Samples `num_items` items from this buffer. Samples in the results may be repeated. Examples for storage of SamplesBatches: - If storage unit `timesteps` has been chosen and batches of size 5 have been added, sample(5) will yield a concatenated batch of 15 timesteps. - If storage unit 'sequences' has been chosen and sequences of different lengths have been added, sample(5) will yield a concatenated batch with a number of timesteps equal to the sum of timesteps in the 5 sampled sequences. - If storage unit 'episodes' has been chosen and episodes of different lengths have been added, sample(5) will yield a concatenated batch with a number of timesteps equal to the sum of timesteps in the 5 sampled episodes. Args: num_items: Number of items to sample from this buffer. **kwargs: Forward compatibility kwargs. Returns: Concatenated batch of items. """ idxes = [random.randint(0, len(self) - 1) for _ in range(num_items)] sample = self._encode_sample(idxes) self._num_timesteps_sampled += sample.count return sample @DeveloperAPI def stats(self, debug: bool = False) -> dict: """Returns the stats of this buffer. Args: debug: If True, adds sample eviction statistics to the returned stats dict. Returns: A dictionary of stats about this buffer. """ data = { "added_count": self._num_timesteps_added, "added_count_wrapped": self._num_timesteps_added_wrap, "eviction_started": self._eviction_started, "sampled_count": self._num_timesteps_sampled, "est_size_bytes": self._est_size_bytes, "num_entries": len(self._storage), } if debug: data.update(self._evicted_hit_stats.stats()) return data @DeveloperAPI def get_state(self) -> Dict[str, Any]: """Returns all local state. Returns: The serializable local state. """ state = {"_storage": self._storage, "_next_idx": self._next_idx} state.update(self.stats(debug=False)) return state @DeveloperAPI def set_state(self, state: Dict[str, Any]) -> None: """Restores all local state to the provided `state`. Args: state: The new state to set this buffer. Can be obtained by calling `self.get_state()`. """ # The actual storage. self._storage = state["_storage"] self._next_idx = state["_next_idx"] # Stats and counts. self._num_timesteps_added = state["added_count"] self._num_timesteps_added_wrap = state["added_count_wrapped"] self._eviction_started = state["eviction_started"] self._num_timesteps_sampled = state["sampled_count"] self._est_size_bytes = state["est_size_bytes"] @DeveloperAPI def _encode_sample(self, idxes: List[int]) -> SampleBatchType: """Fetches concatenated samples at given indeces from the storage.""" samples = [] for i in idxes: self._hit_count[i] += 1 samples.append(self._storage[i]) if samples: # We assume all samples are of same type sample_type = type(samples[0]) out = sample_type.concat_samples(samples) else: out = SampleBatch() out.decompress_if_needed() return out @DeveloperAPI def get_host(self) -> str: """Returns the computer's network name. Returns: The computer's networks name or an empty string, if the network name could not be determined. """ return platform.node() @DeveloperAPI def apply( self, func: Callable[["ReplayBuffer", Optional[Any], Optional[Any]], T], *_args, **kwargs, ) -> T: """Calls the given function with this ReplayBuffer instance. This is useful if we want to apply a function to a set of remote actors. Args: func: A callable that accepts the replay buffer itself, args and kwargs *_arkgs: Any args to pass to func **kwargs: Any kwargs to pass to func Returns: Return value of the induced function call """ return func(self, *_args, **kwargs) @Deprecated(old="ReplayBuffer.add_batch()", new="ReplayBuffer.add()", error=False) def add_batch(self, *args, **kwargs): return self.add(*args, **kwargs) @Deprecated( old="ReplayBuffer.replay(num_items)", new="ReplayBuffer.sample(num_items)", error=False, ) def replay(self, num_items): return self.sample(num_items) @Deprecated( help="ReplayBuffers could be iterated over by default before. " "Making a buffer an iterator will soon " "be deprecated altogether. Consider switching to the training " "iteration API to resolve this.", error=False, ) def make_iterator(self, num_items_to_replay: int): """Make this buffer a ParallelIteratorWorker to retain compatibility. Execution plans have made heavy use of buffers as ParallelIteratorWorkers. This method provides an easy way to support this for now. """ def gen_replay(): while True: yield self.sample(num_items_to_replay) ParallelIteratorWorker.__init__(self, gen_replay, False)