233 lines
7.6 KiB
Python
233 lines
7.6 KiB
Python
"""
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The code is from stable_baseline3.
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"""
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from abc import ABC, abstractmethod
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from gym import spaces
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from typing import Any, Dict, Generator, List, Optional, Union, NamedTuple
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from stable_baselines3.common.vec_env import VecNormalize
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import torch as th
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import numpy as np
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import warnings
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from stable_baselines3.common.type_aliases import (
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ReplayBufferSamples,
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RolloutBufferSamples,
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)
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try:
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# Check memory used by replay buffer when possible
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import psutil
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except ImportError:
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psutil = None
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class BaseBuffer(ABC):
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"""
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Base class that represent a buffer (rollout or replay)
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:param buffer_size: Max number of element in the buffer
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:param observation_dim: Observation space
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:param action_space: Action space
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:param device: PyTorch device
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to which the values will be converted
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:param n_envs: Number of parallel environments
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"""
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def __init__(
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self,
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buffer_size: int,
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observation_dim: int,
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device: Union[th.device, str] = "cpu",
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n_envs: int = 1,
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):
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super(BaseBuffer, self).__init__()
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self.buffer_size = buffer_size
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self.observation_dim = observation_dim
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self.pos = 0
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self.full = False
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self.device = device
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self.n_envs = n_envs
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@staticmethod
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def swap_and_flatten(arr: np.ndarray) -> np.ndarray:
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"""
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Swap and then flatten axes 0 (buffer_size) and 1 (n_envs)
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to convert shape from [n_steps, n_envs, ...] (when ... is the shape of the features)
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to [n_steps * n_envs, ...] (which maintain the order)
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:param arr:
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:return:
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"""
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shape = arr.shape
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if len(shape) < 3:
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shape = shape + (1,)
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return arr.swapaxes(0, 1).reshape(shape[0] * shape[1], *shape[2:])
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def size(self) -> int:
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"""
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:return: The current size of the buffer
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"""
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if self.full:
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return self.buffer_size
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return self.pos
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def add(self, *args, **kwargs) -> None:
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"""
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Add elements to the buffer.
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"""
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raise NotImplementedError()
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def extend(self, *args, **kwargs) -> None:
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"""
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Add a new batch of transitions to the buffer
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"""
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# Do a for loop along the batch axis
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for data in zip(*args):
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self.add(*data)
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def reset(self) -> None:
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"""
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Reset the buffer.
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"""
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self.pos = 0
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self.full = False
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def sample(self, batch_size: int, env: Optional[VecNormalize] = None):
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"""
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:param batch_size: Number of element to sample
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:param env: associated gym VecEnv
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to normalize the observations/rewards when sampling
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:return:
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"""
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upper_bound = self.buffer_size if self.full else self.pos
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batch_inds = np.random.randint(0, upper_bound, size=batch_size)
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return self._get_samples(batch_inds, env=env)
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@abstractmethod
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def _get_samples(
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self, batch_inds: np.ndarray, env: Optional[VecNormalize] = None
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) -> Union[ReplayBufferSamples, RolloutBufferSamples]:
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"""
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:param batch_inds:
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:param env:
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:return:
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"""
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raise NotImplementedError()
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def to_torch(self, array: np.ndarray, copy: bool = True) -> th.Tensor:
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"""
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Convert a numpy array to a PyTorch tensor.
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Note: it copies the data by default
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:param array:
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:param copy: Whether to copy or not the data
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(may be useful to avoid changing things be reference)
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:return:
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"""
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if copy:
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return th.tensor(array).to(self.device)
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return th.as_tensor(array).to(self.device)
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class ReplayBufferSamples(NamedTuple):
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observations: th.Tensor
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goals: th.Tensor
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depths: th.Tensor
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map_id: th.Tensor
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class ReplayBuffer(BaseBuffer):
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"""
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self.observations
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self.goals
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self.depths
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self.map_ids
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"""
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def __init__(
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self,
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buffer_size: int,
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observation_dim: spaces.Space,
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image_WxH: tuple,
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device: Union[th.device, str] = "cpu",
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n_envs: int = 1,
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optimize_memory_usage: bool = False,
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):
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super(ReplayBuffer, self).__init__(buffer_size, observation_dim, device, n_envs=n_envs)
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# Adjust buffer size
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self.buffer_size = max(buffer_size // n_envs, 1)
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# Check that the replay buffer can fit into the memory
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if psutil is not None:
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mem_available = psutil.virtual_memory().available
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self.optimize_memory_usage = optimize_memory_usage
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self.observations = np.zeros((self.buffer_size, self.n_envs, observation_dim), dtype=np.float32)
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self.goals = np.zeros((self.buffer_size, self.n_envs, 3), dtype=np.float32)
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self.depths = np.zeros((self.buffer_size, self.n_envs, 1, image_WxH[1], image_WxH[0]), dtype=np.float32)
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self.map_ids = np.zeros((self.buffer_size, self.n_envs, 1), dtype=np.int16)
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if psutil is not None:
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total_memory_usage = self.observations.nbytes + self.goals.nbytes + self.depths.nbytes + self.map_ids.nbytes
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if total_memory_usage > mem_available:
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# Convert to GB
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total_memory_usage /= 1e9
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mem_available /= 1e9
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warnings.warn(
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"This system does not have apparently enough memory to store the complete "
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f"replay buffer {total_memory_usage:.2f}GB > {mem_available:.2f}GB"
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)
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def add(self,
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obs: np.ndarray,
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goal: np.ndarray,
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depth: np.ndarray,
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map_id: int) -> None:
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# Copy to avoid modification by reference
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self.observations[self.pos] = np.array(obs).copy()
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self.goals[self.pos] = np.array(goal).copy()
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self.depths[self.pos] = np.array(depth).copy()
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self.map_ids[self.pos] = np.array(map_id).copy()
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self.pos += 1
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if self.pos == self.buffer_size:
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self.full = True
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self.pos = 0
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def sample(self, batch_size: int, env: Optional[VecNormalize] = None) -> ReplayBufferSamples:
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"""
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Sample elements from the replay buffer.
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Custom sampling when using memory efficient variant,
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as we should not sample the element with index `self.pos`
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See https://github.com/DLR-RM/stable-baselines3/pull/28#issuecomment-637559274
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:param batch_size: Number of element to sample
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:param env: associated gym VecEnv
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to normalize the observations/rewards when sampling
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:return:
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"""
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if not self.optimize_memory_usage:
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return super().sample(batch_size=batch_size, env=env)
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# Do not sample the element with index `self.pos` as the transitions is invalid
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# (we use only one array to store `obs` and `next_obs`)
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if self.full:
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batch_inds = (np.random.randint(1, self.buffer_size, size=batch_size) + self.pos) % self.buffer_size
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else:
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batch_inds = np.random.randint(0, self.pos, size=batch_size)
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return self._get_samples(batch_inds, env=env)
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def _get_samples(self, batch_inds: np.ndarray, env: Optional[VecNormalize] = None) -> ReplayBufferSamples:
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env_indices = np.random.randint(0, high=self.n_envs, size=(len(batch_inds),))
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data = (
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self.observations[batch_inds, env_indices, :],
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self.goals[batch_inds, env_indices, :],
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self.depths[batch_inds, env_indices, :],
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self.map_ids[batch_inds, env_indices, :],
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)
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return ReplayBufferSamples(*data)
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