mirror of
https://github.com/saymrwulf/stable-baselines3.git
synced 2026-05-18 21:30:19 +00:00
261 lines
11 KiB
Python
261 lines
11 KiB
Python
import numpy as np
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import torch as th
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class BaseBuffer(object):
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"""
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Base class that represent a buffer (rollout or replay)
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:param buffer_size: (int) Max number of element in the buffer
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:param obs_dim: (int) Dimension of the observation
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:param action_dim: (int) Dimension of the action space
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:param device: (th.device)
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:param n_envs: (int) Number of parallel environments
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"""
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def __init__(self, buffer_size, obs_dim, action_dim, device='cpu', n_envs=1):
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super(BaseBuffer, self).__init__()
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self.buffer_size = buffer_size
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self.obs_dim = obs_dim
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self.action_dim = action_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(tensor):
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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 tensor: (th.Tensor)
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:return: (th.Tensor)
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"""
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shape = tensor.shape
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if len(shape) < 3:
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shape = shape + (1,)
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return tensor.transpose(0, 1).reshape(shape[0] * shape[1], *shape[2:])
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def size(self):
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"""
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:return: (int) 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):
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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 reset(self):
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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, env=None):
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"""
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:param batch_size: (int) Number of element to sample
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:param env: (VecNormalize) [Optional] associated gym VecEnv
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to normalize the observations/rewards when sampling
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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 = th.LongTensor(
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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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def _get_samples(self, batch_inds, env=None):
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"""
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:param batch_inds: (th.Tensor)
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:param env: (gym.Env)
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:return: ([th.Tensor])
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"""
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raise NotImplementedError()
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@staticmethod
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def _normalize_obs(obs, env=None):
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if env is not None:
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# TODO: get rid of pytorch - numpy conversion
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return th.FloatTensor(env.normalize_obs(obs.numpy()))
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return obs
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def _normalize_reward(self, reward, env=None):
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if env is not None:
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return th.FloatTensor(env.normalize_reward(reward.numpy()))
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return reward
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class ReplayBuffer(BaseBuffer):
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"""
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Replay buffer used in off-policy algorithms like SAC/TD3.
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Adapted from from https://github.com/apourchot/CEM-RL
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:param buffer_size: (int) Max number of element in the buffer
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:param obs_dim: (int) Dimension of the observation
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:param action_dim: (int) Dimension of the action space
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:param device: (th.device)
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:param n_envs: (int) Number of parallel environments
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"""
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def __init__(self, buffer_size, obs_dim, action_dim, device='cpu', n_envs=1):
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super(ReplayBuffer, self).__init__(buffer_size, obs_dim, action_dim, device, n_envs=n_envs)
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assert n_envs == 1
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self.observations = th.zeros(self.buffer_size, self.n_envs, self.obs_dim)
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self.actions = th.zeros(self.buffer_size, self.n_envs, self.action_dim)
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self.next_observations = th.zeros(self.buffer_size, self.n_envs, self.obs_dim)
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self.rewards = th.zeros(self.buffer_size, self.n_envs)
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self.dones = th.zeros(self.buffer_size, self.n_envs)
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def add(self, obs, next_obs, action, reward, done):
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# Copy to avoid modification by reference
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self.observations[self.pos] = th.FloatTensor(np.array(obs).copy())
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self.next_observations[self.pos] = th.FloatTensor(np.array(next_obs).copy())
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self.actions[self.pos] = th.FloatTensor(np.array(action).copy())
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self.rewards[self.pos] = th.FloatTensor(np.array(reward).copy())
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self.dones[self.pos] = th.FloatTensor(np.array(done).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 _get_samples(self, batch_inds, env=None):
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return (self._normalize_obs(self.observations[batch_inds, 0, :], env).to(self.device),
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self.actions[batch_inds, 0, :].to(self.device),
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self._normalize_obs(self.next_observations[batch_inds, 0, :], env).to(self.device),
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self.dones[batch_inds].to(self.device),
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self._normalize_reward(self.rewards[batch_inds], env).to(self.device))
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class RolloutBuffer(BaseBuffer):
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"""
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Rollout buffer used in on-policy algorithms like A2C/PPO.
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:param buffer_size: (int) Max number of element in the buffer
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:param obs_dim: (int) Dimension of the observation
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:param action_dim: (int) Dimension of the action space
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:param device: (th.device)
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:param gae_lambda: (float) Factor for trade-off of bias vs variance for Generalized Advantage Estimator
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Equivalent to classic advantage when set to 1.
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:param gamma: (float) Discount factor
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:param n_envs: (int) Number of parallel environments
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"""
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def __init__(self, buffer_size, obs_dim, action_dim, device='cpu',
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gae_lambda=1, gamma=0.99, n_envs=1):
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super(RolloutBuffer, self).__init__(buffer_size, obs_dim, action_dim, device, n_envs=n_envs)
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# TODO: try the buffer on the gpu?
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self.gae_lambda = gae_lambda
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self.gamma = gamma
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self.observations, self.actions, self.rewards, self.advantages = None, None, None, None
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self.returns, self.dones, self.values, self.log_probs = None, None, None, None
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self.generator_ready = False
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self.reset()
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def reset(self):
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self.observations = th.zeros(self.buffer_size, self.n_envs, self.obs_dim)
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self.actions = th.zeros(self.buffer_size, self.n_envs, self.action_dim)
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self.rewards = th.zeros(self.buffer_size, self.n_envs)
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self.returns = th.zeros(self.buffer_size, self.n_envs)
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self.dones = th.zeros(self.buffer_size, self.n_envs)
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self.values = th.zeros(self.buffer_size, self.n_envs)
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self.log_probs = th.zeros(self.buffer_size, self.n_envs)
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self.advantages = th.zeros(self.buffer_size, self.n_envs)
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self.generator_ready = False
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super(RolloutBuffer, self).reset()
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def compute_returns_and_advantage(self, last_value, dones=False, use_gae=True):
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"""
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Post-processing step: compute the returns (sum of discounted rewards)
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and advantage (A(s) = R - V(S)).
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Adapted from Stable-Baselines PPO2.
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:param last_value: (th.Tensor)
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:param dones: ([bool])
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:param use_gae: (bool) Whether to use Generalized Advantage Estimation
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or normal advantage for advantage computation.
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"""
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if use_gae:
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last_gae_lam = 0
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for step in reversed(range(self.buffer_size)):
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if step == self.buffer_size - 1:
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next_non_terminal = th.FloatTensor(1.0 - dones)
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next_value = last_value.clone().cpu().flatten()
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else:
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next_non_terminal = 1.0 - self.dones[step + 1]
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next_value = self.values[step + 1]
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delta = self.rewards[step] + self.gamma * next_value * next_non_terminal - self.values[step]
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last_gae_lam = delta + self.gamma * self.gae_lambda * next_non_terminal * last_gae_lam
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self.advantages[step] = last_gae_lam
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self.returns = self.advantages + self.values
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else:
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# Discounted return with value bootstrap
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# Note: this is equivalent to GAE computation
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# with gae_lambda = 1.0
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last_return = 0.0
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for step in reversed(range(self.buffer_size)):
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if step == self.buffer_size - 1:
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next_non_terminal = th.FloatTensor(1.0 - dones)
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next_value = last_value.clone().cpu().flatten()
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last_return = self.rewards[step] + next_non_terminal * next_value
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else:
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next_non_terminal = 1.0 - self.dones[step + 1]
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last_return = self.rewards[step] + self.gamma * last_return * next_non_terminal
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self.returns[step] = last_return
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self.advantages = self.returns - self.values
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def add(self, obs, action, reward, done, value, log_prob):
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"""
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:param obs: (np.ndarray) Observation
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:param action: (np.ndarray) Action
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:param reward: (np.ndarray)
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:param done: (np.ndarray) End of episode signal.
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:param value: (th.Tensor) estimated value of the current state
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following the current policy.
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:param log_prob: (th.Tensor) log probability of the action
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following the current policy.
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"""
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if len(log_prob.shape) == 0:
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# Reshape 0-d tensor to avoid error
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log_prob = log_prob.reshape(-1, 1)
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self.observations[self.pos] = th.FloatTensor(np.array(obs).copy())
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self.actions[self.pos] = th.FloatTensor(np.array(action).copy())
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self.rewards[self.pos] = th.FloatTensor(np.array(reward).copy())
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self.dones[self.pos] = th.FloatTensor(np.array(done).copy())
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self.values[self.pos] = th.FloatTensor(value.clone().cpu().flatten())
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self.log_probs[self.pos] = th.FloatTensor(log_prob.cpu().clone())
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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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def get(self, batch_size=None):
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assert self.full
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indices = th.randperm(self.buffer_size * self.n_envs)
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# Prepare the data
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if not self.generator_ready:
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for tensor in ['observations', 'actions', 'values',
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'log_probs', 'advantages', 'returns']:
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self.__dict__[tensor] = self.swap_and_flatten(self.__dict__[tensor])
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self.generator_ready = True
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# Return everything, don't create minibatches
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if batch_size is None:
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batch_size = self.buffer_size * self.n_envs
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start_idx = 0
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while start_idx < self.buffer_size * self.n_envs:
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yield self._get_samples(indices[start_idx:start_idx + batch_size])
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start_idx += batch_size
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def _get_samples(self, batch_inds, env=None):
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return (self.observations[batch_inds].to(self.device),
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self.actions[batch_inds].to(self.device),
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self.values[batch_inds].flatten().to(self.device),
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self.log_probs[batch_inds].flatten().to(self.device),
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self.advantages[batch_inds].flatten().to(self.device),
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self.returns[batch_inds].flatten().to(self.device))
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