from collections import defaultdict from collections.abc import MutableMapping import enum import itertools from typing import BinaryIO, DefaultDict, List, Tuple, Union, Optional import numpy as np import h5py from mlagents_envs.exception import UnityException class BufferException(UnityException): """ Related to errors with the Buffer. """ pass class BufferKey(enum.Enum): ACTION_MASK = "action_mask" CONTINUOUS_ACTION = "continuous_action" CONTINUOUS_LOG_PROBS = "continuous_log_probs" DISCRETE_ACTION = "discrete_action" DISCRETE_LOG_PROBS = "discrete_log_probs" DONE = "done" ENVIRONMENT_REWARDS = "environment_rewards" MASKS = "masks" MEMORY = "memory" CRITIC_MEMORY = "critic_memory" PREV_ACTION = "prev_action" ADVANTAGES = "advantages" DISCOUNTED_RETURNS = "discounted_returns" class ObservationKeyPrefix(enum.Enum): OBSERVATION = "obs" NEXT_OBSERVATION = "next_obs" class RewardSignalKeyPrefix(enum.Enum): # Reward signals REWARDS = "rewards" VALUE_ESTIMATES = "value_estimates" RETURNS = "returns" ADVANTAGE = "advantage" AgentBufferKey = Union[ BufferKey, Tuple[ObservationKeyPrefix, int], Tuple[RewardSignalKeyPrefix, str] ] class RewardSignalUtil: @staticmethod def rewards_key(name: str) -> AgentBufferKey: return RewardSignalKeyPrefix.REWARDS, name @staticmethod def value_estimates_key(name: str) -> AgentBufferKey: return RewardSignalKeyPrefix.RETURNS, name @staticmethod def returns_key(name: str) -> AgentBufferKey: return RewardSignalKeyPrefix.RETURNS, name @staticmethod def advantage_key(name: str) -> AgentBufferKey: return RewardSignalKeyPrefix.ADVANTAGE, name class AgentBufferField(list): """ AgentBufferField is a list of numpy arrays. When an agent collects a field, you can add it to its AgentBufferField with the append method. """ def __init__(self): self.padding_value = 0 super().__init__() def __str__(self): return str(np.array(self).shape) def append(self, element: np.ndarray, padding_value: float = 0.0) -> None: """ Adds an element to this list. Also lets you change the padding type, so that it can be set on append (e.g. action_masks should be padded with 1.) :param element: The element to append to the list. :param padding_value: The value used to pad when get_batch is called. """ super().append(element) self.padding_value = padding_value def extend(self, data: np.ndarray) -> None: """ Adds a list of np.arrays to the end of the list of np.arrays. :param data: The np.array list to append. """ self += list(np.array(data, dtype=np.float32)) def set(self, data): """ Sets the list of np.array to the input data :param data: The np.array list to be set. """ # Make sure we convert incoming data to float32 if it's a float dtype = None if data is not None and len(data) and isinstance(data[0], float): dtype = np.float32 self[:] = [] self[:] = list(np.array(data, dtype=dtype)) def get_batch( self, batch_size: int = None, training_length: Optional[int] = 1, sequential: bool = True, ) -> np.ndarray: """ Retrieve the last batch_size elements of length training_length from the list of np.array :param batch_size: The number of elements to retrieve. If None: All elements will be retrieved. :param training_length: The length of the sequence to be retrieved. If None: only takes one element. :param sequential: If true and training_length is not None: the elements will not repeat in the sequence. [a,b,c,d,e] with training_length = 2 and sequential=True gives [[0,a],[b,c],[d,e]]. If sequential=False gives [[a,b],[b,c],[c,d],[d,e]] """ if training_length is None: training_length = 1 if sequential: # The sequences will not have overlapping elements (this involves padding) leftover = len(self) % training_length # leftover is the number of elements in the first sequence (this sequence might need 0 padding) if batch_size is None: # retrieve the maximum number of elements batch_size = len(self) // training_length + 1 * (leftover != 0) # The maximum number of sequences taken from a list of length len(self) without overlapping # with padding is equal to batch_size if batch_size > (len(self) // training_length + 1 * (leftover != 0)): raise BufferException( "The batch size and training length requested for get_batch where" " too large given the current number of data points." ) if batch_size * training_length > len(self): padding = np.array(self[-1], dtype=np.float32) * self.padding_value return np.array( [padding] * (training_length - leftover) + self[:], dtype=np.float32 ) else: return np.array( self[len(self) - batch_size * training_length :], dtype=np.float32 ) else: # The sequences will have overlapping elements if batch_size is None: # retrieve the maximum number of elements batch_size = len(self) - training_length + 1 # The number of sequences of length training_length taken from a list of len(self) elements # with overlapping is equal to batch_size if (len(self) - training_length + 1) < batch_size: raise BufferException( "The batch size and training length requested for get_batch where" " too large given the current number of data points." ) tmp_list: List[np.ndarray] = [] for end in range(len(self) - batch_size + 1, len(self) + 1): tmp_list += self[end - training_length : end] return np.array(tmp_list, dtype=np.float32) def reset_field(self) -> None: """ Resets the AgentBufferField """ self[:] = [] class AgentBuffer(MutableMapping): """ AgentBuffer contains a dictionary of AgentBufferFields. Each agent has his own AgentBuffer. The keys correspond to the name of the field. Example: state, action """ # Whether or not to validate the types of keys at runtime # This should be off for training, but enabled for testing CHECK_KEY_TYPES_AT_RUNTIME = False def __init__(self): self.last_brain_info = None self.last_take_action_outputs = None self._fields: DefaultDict[AgentBufferKey, AgentBufferField] = defaultdict( AgentBufferField ) def __str__(self): return ", ".join( ["'{}' : {}".format(k, str(self[k])) for k in self._fields.keys()] ) def reset_agent(self) -> None: """ Resets the AgentBuffer """ for f in self._fields.values(): f.reset_field() self.last_brain_info = None self.last_take_action_outputs = None @staticmethod def _check_key(key): if isinstance(key, BufferKey): return if isinstance(key, tuple): key0, key1 = key if isinstance(key0, ObservationKeyPrefix): if isinstance(key1, int): return raise KeyError(f"{key} has type ({type(key0)}, {type(key1)})") if isinstance(key0, RewardSignalKeyPrefix): if isinstance(key1, str): return raise KeyError(f"{key} has type ({type(key0)}, {type(key1)})") raise KeyError(f"{key} is a {type(key)}") @staticmethod def _encode_key(key: AgentBufferKey) -> str: """ Convert the key to a string representation so that it can be used for serialization. """ if isinstance(key, BufferKey): return key.value prefix, suffix = key return f"{prefix.value}:{suffix}" @staticmethod def _decode_key(encoded_key: str) -> AgentBufferKey: """ Convert the string representation back to a key after serialization. """ # Simple case: convert the string directly to a BufferKey try: return BufferKey(encoded_key) except ValueError: pass # Not a simple key, so split into two parts prefix_str, _, suffix_str = encoded_key.partition(":") # See if it's an ObservationKeyPrefix first try: return ObservationKeyPrefix(prefix_str), int(suffix_str) except ValueError: pass # If not, it had better be a RewardSignalKeyPrefix try: return RewardSignalKeyPrefix(prefix_str), suffix_str except ValueError: raise ValueError(f"Unable to convert {encoded_key} to an AgentBufferKey") def __getitem__(self, key: AgentBufferKey) -> AgentBufferField: if self.CHECK_KEY_TYPES_AT_RUNTIME: self._check_key(key) return self._fields[key] def __setitem__(self, key: AgentBufferKey, value: AgentBufferField) -> None: if self.CHECK_KEY_TYPES_AT_RUNTIME: self._check_key(key) self._fields[key] = value def __delitem__(self, key: AgentBufferKey) -> None: if self.CHECK_KEY_TYPES_AT_RUNTIME: self._check_key(key) self._fields.__delitem__(key) def __iter__(self): return self._fields.__iter__() def __len__(self) -> int: return self._fields.__len__() def __contains__(self, key): if self.CHECK_KEY_TYPES_AT_RUNTIME: self._check_key(key) return self._fields.__contains__(key) def check_length(self, key_list: List[AgentBufferKey]) -> bool: """ Some methods will require that some fields have the same length. check_length will return true if the fields in key_list have the same length. :param key_list: The fields which length will be compared """ if self.CHECK_KEY_TYPES_AT_RUNTIME: for k in key_list: self._check_key(k) if len(key_list) < 2: return True length = None for key in key_list: if key not in self._fields: return False if (length is not None) and (length != len(self[key])): return False length = len(self[key]) return True def shuffle( self, sequence_length: int, key_list: List[AgentBufferKey] = None ) -> None: """ Shuffles the fields in key_list in a consistent way: The reordering will be the same across fields. :param key_list: The fields that must be shuffled. """ if key_list is None: key_list = list(self._fields.keys()) if not self.check_length(key_list): raise BufferException( "Unable to shuffle if the fields are not of same length" ) s = np.arange(len(self[key_list[0]]) // sequence_length) np.random.shuffle(s) for key in key_list: tmp: List[np.ndarray] = [] for i in s: tmp += self[key][i * sequence_length : (i + 1) * sequence_length] self[key][:] = tmp def make_mini_batch(self, start: int, end: int) -> "AgentBuffer": """ Creates a mini-batch from buffer. :param start: Starting index of buffer. :param end: Ending index of buffer. :return: Dict of mini batch. """ mini_batch = AgentBuffer() for key, field in self._fields.items(): # slicing AgentBufferField returns a List[Any} mini_batch[key] = field[start:end] # type: ignore return mini_batch def sample_mini_batch( self, batch_size: int, sequence_length: int = 1 ) -> "AgentBuffer": """ Creates a mini-batch from a random start and end. :param batch_size: number of elements to withdraw. :param sequence_length: Length of sequences to sample. Number of sequences to sample will be batch_size/sequence_length. """ num_seq_to_sample = batch_size // sequence_length mini_batch = AgentBuffer() buff_len = self.num_experiences num_sequences_in_buffer = buff_len // sequence_length start_idxes = ( np.random.randint(num_sequences_in_buffer, size=num_seq_to_sample) * sequence_length ) # Sample random sequence starts for key in self: mb_list = [self[key][i : i + sequence_length] for i in start_idxes] # See comparison of ways to make a list from a list of lists here: # https://stackoverflow.com/questions/952914/how-to-make-a-flat-list-out-of-list-of-lists mini_batch[key].set(list(itertools.chain.from_iterable(mb_list))) return mini_batch def save_to_file(self, file_object: BinaryIO) -> None: """ Saves the AgentBuffer to a file-like object. """ with h5py.File(file_object, "w") as write_file: for key, data in self.items(): write_file.create_dataset( self._encode_key(key), data=data, dtype="f", compression="gzip" ) def load_from_file(self, file_object: BinaryIO) -> None: """ Loads the AgentBuffer from a file-like object. """ with h5py.File(file_object, "r") as read_file: for key in list(read_file.keys()): decoded_key = self._decode_key(key) self[decoded_key] = AgentBufferField() # extend() will convert the numpy array's first dimension into list self[decoded_key].extend(read_file[key][()]) def truncate(self, max_length: int, sequence_length: int = 1) -> None: """ Truncates the buffer to a certain length. This can be slow for large buffers. We compensate by cutting further than we need to, so that we're not truncating at each update. Note that we must truncate an integer number of sequence_lengths param: max_length: The length at which to truncate the buffer. """ current_length = self.num_experiences # make max_length an integer number of sequence_lengths max_length -= max_length % sequence_length if current_length > max_length: for _key in self.keys(): self[_key][:] = self[_key][current_length - max_length :] def resequence_and_append( self, target_buffer: "AgentBuffer", key_list: List[AgentBufferKey] = None, batch_size: int = None, training_length: int = None, ) -> None: """ Takes in a batch size and training length (sequence length), and appends this AgentBuffer to target_buffer properly padded for LSTM use. Optionally, use key_list to restrict which fields are inserted into the new buffer. :param target_buffer: The buffer which to append the samples to. :param key_list: The fields that must be added. If None: all fields will be appended. :param batch_size: The number of elements that must be appended. If None: All of them will be. :param training_length: The length of the samples that must be appended. If None: only takes one element. """ if key_list is None: key_list = list(self.keys()) if not self.check_length(key_list): raise BufferException( f"The length of the fields {key_list} were not of same length" ) for field_key in key_list: target_buffer[field_key].extend( self[field_key].get_batch( batch_size=batch_size, training_length=training_length ) ) @property def num_experiences(self) -> int: """ The number of agent experiences in the AgentBuffer, i.e. the length of the buffer. An experience consists of one element across all of the fields of this AgentBuffer. Note that these all have to be the same length, otherwise shuffle and append_to_update_buffer will fail. """ if self.values(): return len(next(iter(self.values()))) else: return 0