17ef4dd5eb
* Implement example mujoco_redq_hl * Add abstraction CriticEnsembleFactory with default implementations to suit REDQ * Fix type annotation of linear_layer in Net, MLP, Critic (was incompatible with REDQ usage)
506 lines
17 KiB
Python
506 lines
17 KiB
Python
import warnings
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from collections.abc import Sequence
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from typing import Any, cast
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import numpy as np
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import torch
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from torch import nn
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from tianshou.utils.net.common import MLP, BaseActor, TActionShape, TLinearLayer
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SIGMA_MIN = -20
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SIGMA_MAX = 2
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class Actor(BaseActor):
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"""Simple actor network.
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It will create an actor operated in continuous action space with structure of preprocess_net ---> action_shape.
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:param preprocess_net: a self-defined preprocess_net which output a
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flattened hidden state.
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:param action_shape: a sequence of int for the shape of action.
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:param hidden_sizes: a sequence of int for constructing the MLP after
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preprocess_net. Default to empty sequence (where the MLP now contains
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only a single linear layer).
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:param max_action: the scale for the final action logits. Default to
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1.
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:param preprocess_net_output_dim: the output dimension of
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preprocess_net.
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For advanced usage (how to customize the network), please refer to
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:ref:`build_the_network`.
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.. seealso::
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Please refer to :class:`~tianshou.utils.net.common.Net` as an instance
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of how preprocess_net is suggested to be defined.
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"""
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def __init__(
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self,
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preprocess_net: nn.Module,
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action_shape: TActionShape,
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hidden_sizes: Sequence[int] = (),
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max_action: float = 1.0,
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device: str | int | torch.device = "cpu",
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preprocess_net_output_dim: int | None = None,
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) -> None:
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super().__init__()
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self.device = device
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self.preprocess = preprocess_net
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self.output_dim = int(np.prod(action_shape))
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input_dim = getattr(preprocess_net, "output_dim", preprocess_net_output_dim)
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input_dim = cast(int, input_dim)
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self.last = MLP(
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input_dim,
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self.output_dim,
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hidden_sizes,
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device=self.device,
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)
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self.max_action = max_action
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def get_preprocess_net(self) -> nn.Module:
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return self.preprocess
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def forward(
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self,
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obs: np.ndarray | torch.Tensor,
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state: Any = None,
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info: dict[str, Any] | None = None,
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) -> tuple[torch.Tensor, Any]:
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"""Mapping: obs -> logits -> action."""
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if info is None:
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info = {}
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logits, hidden = self.preprocess(obs, state)
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logits = self.max_action * torch.tanh(self.last(logits))
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return logits, hidden
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class Critic(nn.Module):
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"""Simple critic network.
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It will create an actor operated in continuous action space with structure of preprocess_net ---> 1(q value).
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:param preprocess_net: a self-defined preprocess_net which output a
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flattened hidden state.
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:param hidden_sizes: a sequence of int for constructing the MLP after
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preprocess_net. Default to empty sequence (where the MLP now contains
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only a single linear layer).
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:param preprocess_net_output_dim: the output dimension of
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preprocess_net.
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:param linear_layer: use this module as linear layer. Default to nn.Linear.
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:param flatten_input: whether to flatten input data for the last layer.
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Default to True.
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For advanced usage (how to customize the network), please refer to
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:ref:`build_the_network`.
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.. seealso::
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Please refer to :class:`~tianshou.utils.net.common.Net` as an instance
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of how preprocess_net is suggested to be defined.
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"""
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def __init__(
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self,
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preprocess_net: nn.Module,
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hidden_sizes: Sequence[int] = (),
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device: str | int | torch.device = "cpu",
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preprocess_net_output_dim: int | None = None,
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linear_layer: TLinearLayer = nn.Linear,
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flatten_input: bool = True,
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) -> None:
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super().__init__()
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self.device = device
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self.preprocess = preprocess_net
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self.output_dim = 1
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input_dim = getattr(preprocess_net, "output_dim", preprocess_net_output_dim)
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self.last = MLP(
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input_dim, # type: ignore
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1,
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hidden_sizes,
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device=self.device,
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linear_layer=linear_layer,
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flatten_input=flatten_input,
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)
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def forward(
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self,
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obs: np.ndarray | torch.Tensor,
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act: np.ndarray | torch.Tensor | None = None,
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info: dict[str, Any] | None = None,
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) -> torch.Tensor:
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"""Mapping: (s, a) -> logits -> Q(s, a)."""
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if info is None:
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info = {}
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obs = torch.as_tensor(
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obs,
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device=self.device,
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dtype=torch.float32,
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).flatten(1)
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if act is not None:
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act = torch.as_tensor(
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act,
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device=self.device,
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dtype=torch.float32,
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).flatten(1)
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obs = torch.cat([obs, act], dim=1)
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logits, hidden = self.preprocess(obs)
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return self.last(logits)
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class ActorProb(BaseActor):
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"""Simple actor network (output with a Gauss distribution).
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:param preprocess_net: a self-defined preprocess_net which output a
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flattened hidden state.
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:param action_shape: a sequence of int for the shape of action.
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:param hidden_sizes: a sequence of int for constructing the MLP after
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preprocess_net. Default to empty sequence (where the MLP now contains
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only a single linear layer).
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:param max_action: the scale for the final action logits. Default to
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1.
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:param unbounded: whether to apply tanh activation on final logits.
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Default to False.
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:param conditioned_sigma: True when sigma is calculated from the
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input, False when sigma is an independent parameter. Default to False.
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:param preprocess_net_output_dim: the output dimension of
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preprocess_net.
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For advanced usage (how to customize the network), please refer to
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:ref:`build_the_network`.
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.. seealso::
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Please refer to :class:`~tianshou.utils.net.common.Net` as an instance
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of how preprocess_net is suggested to be defined.
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"""
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def __init__(
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self,
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preprocess_net: nn.Module,
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action_shape: TActionShape,
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hidden_sizes: Sequence[int] = (),
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max_action: float = 1.0,
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device: str | int | torch.device = "cpu",
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unbounded: bool = False,
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conditioned_sigma: bool = False,
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preprocess_net_output_dim: int | None = None,
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) -> None:
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super().__init__()
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if unbounded and not np.isclose(max_action, 1.0):
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warnings.warn("Note that max_action input will be discarded when unbounded is True.")
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max_action = 1.0
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self.preprocess = preprocess_net
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self.device = device
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self.output_dim = int(np.prod(action_shape))
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input_dim = getattr(preprocess_net, "output_dim", preprocess_net_output_dim)
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self.mu = MLP(input_dim, self.output_dim, hidden_sizes, device=self.device) # type: ignore
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self._c_sigma = conditioned_sigma
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if conditioned_sigma:
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self.sigma = MLP(
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input_dim, # type: ignore
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self.output_dim,
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hidden_sizes,
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device=self.device,
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)
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else:
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self.sigma_param = nn.Parameter(torch.zeros(self.output_dim, 1))
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self.max_action = max_action
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self._unbounded = unbounded
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def get_preprocess_net(self) -> nn.Module:
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return self.preprocess
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def forward(
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self,
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obs: np.ndarray | torch.Tensor,
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state: Any = None,
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info: dict[str, Any] | None = None,
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) -> tuple[tuple[torch.Tensor, torch.Tensor], Any]:
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"""Mapping: obs -> logits -> (mu, sigma)."""
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if info is None:
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info = {}
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logits, hidden = self.preprocess(obs, state)
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mu = self.mu(logits)
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if not self._unbounded:
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mu = self.max_action * torch.tanh(mu)
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if self._c_sigma:
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sigma = torch.clamp(self.sigma(logits), min=SIGMA_MIN, max=SIGMA_MAX).exp()
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else:
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shape = [1] * len(mu.shape)
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shape[1] = -1
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sigma = (self.sigma_param.view(shape) + torch.zeros_like(mu)).exp()
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return (mu, sigma), state
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class RecurrentActorProb(nn.Module):
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"""Recurrent version of ActorProb.
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For advanced usage (how to customize the network), please refer to
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:ref:`build_the_network`.
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"""
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def __init__(
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self,
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layer_num: int,
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state_shape: Sequence[int],
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action_shape: Sequence[int],
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hidden_layer_size: int = 128,
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max_action: float = 1.0,
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device: str | int | torch.device = "cpu",
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unbounded: bool = False,
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conditioned_sigma: bool = False,
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) -> None:
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super().__init__()
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if unbounded and not np.isclose(max_action, 1.0):
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warnings.warn("Note that max_action input will be discarded when unbounded is True.")
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max_action = 1.0
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self.device = device
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self.nn = nn.LSTM(
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input_size=int(np.prod(state_shape)),
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hidden_size=hidden_layer_size,
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num_layers=layer_num,
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batch_first=True,
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)
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output_dim = int(np.prod(action_shape))
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self.mu = nn.Linear(hidden_layer_size, output_dim)
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self._c_sigma = conditioned_sigma
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if conditioned_sigma:
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self.sigma = nn.Linear(hidden_layer_size, output_dim)
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else:
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self.sigma_param = nn.Parameter(torch.zeros(output_dim, 1))
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self.max_action = max_action
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self._unbounded = unbounded
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def forward(
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self,
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obs: np.ndarray | torch.Tensor,
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state: dict[str, torch.Tensor] | None = None,
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info: dict[str, Any] | None = None,
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) -> tuple[tuple[torch.Tensor, torch.Tensor], dict[str, torch.Tensor]]:
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"""Almost the same as :class:`~tianshou.utils.net.common.Recurrent`."""
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if info is None:
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info = {}
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obs = torch.as_tensor(
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obs,
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device=self.device,
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dtype=torch.float32,
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)
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# obs [bsz, len, dim] (training) or [bsz, dim] (evaluation)
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# In short, the tensor's shape in training phase is longer than which
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# in evaluation phase.
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if len(obs.shape) == 2:
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obs = obs.unsqueeze(-2)
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self.nn.flatten_parameters()
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if state is None:
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obs, (hidden, cell) = self.nn(obs)
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else:
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# we store the stack data in [bsz, len, ...] format
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# but pytorch rnn needs [len, bsz, ...]
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obs, (hidden, cell) = self.nn(
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obs,
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(
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state["hidden"].transpose(0, 1).contiguous(),
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state["cell"].transpose(0, 1).contiguous(),
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),
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)
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logits = obs[:, -1]
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mu = self.mu(logits)
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if not self._unbounded:
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mu = self.max_action * torch.tanh(mu)
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if self._c_sigma:
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sigma = torch.clamp(self.sigma(logits), min=SIGMA_MIN, max=SIGMA_MAX).exp()
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else:
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shape = [1] * len(mu.shape)
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shape[1] = -1
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sigma = (self.sigma_param.view(shape) + torch.zeros_like(mu)).exp()
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# please ensure the first dim is batch size: [bsz, len, ...]
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return (mu, sigma), {
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"hidden": hidden.transpose(0, 1).detach(),
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"cell": cell.transpose(0, 1).detach(),
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}
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class RecurrentCritic(nn.Module):
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"""Recurrent version of Critic.
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For advanced usage (how to customize the network), please refer to
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:ref:`build_the_network`.
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"""
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def __init__(
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self,
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layer_num: int,
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state_shape: Sequence[int],
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action_shape: Sequence[int] = [0],
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device: str | int | torch.device = "cpu",
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hidden_layer_size: int = 128,
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) -> None:
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super().__init__()
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self.state_shape = state_shape
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self.action_shape = action_shape
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self.device = device
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self.nn = nn.LSTM(
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input_size=int(np.prod(state_shape)),
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hidden_size=hidden_layer_size,
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num_layers=layer_num,
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batch_first=True,
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)
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self.fc2 = nn.Linear(hidden_layer_size + int(np.prod(action_shape)), 1)
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def forward(
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self,
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obs: np.ndarray | torch.Tensor,
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act: np.ndarray | torch.Tensor | None = None,
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info: dict[str, Any] | None = None,
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) -> torch.Tensor:
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"""Almost the same as :class:`~tianshou.utils.net.common.Recurrent`."""
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if info is None:
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info = {}
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obs = torch.as_tensor(
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obs,
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device=self.device,
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dtype=torch.float32,
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)
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# obs [bsz, len, dim] (training) or [bsz, dim] (evaluation)
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# In short, the tensor's shape in training phase is longer than which
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# in evaluation phase.
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assert len(obs.shape) == 3
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self.nn.flatten_parameters()
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obs, (hidden, cell) = self.nn(obs)
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obs = obs[:, -1]
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if act is not None:
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act = torch.as_tensor(
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act,
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device=self.device,
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dtype=torch.float32,
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)
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obs = torch.cat([obs, act], dim=1)
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return self.fc2(obs)
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class Perturbation(nn.Module):
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"""Implementation of perturbation network in BCQ algorithm.
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Given a state and action, it can generate perturbed action.
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:param preprocess_net: a self-defined preprocess_net which output a
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flattened hidden state.
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:param max_action: the maximum value of each dimension of action.
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:param device: which device to create this model on.
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Default to cpu.
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:param phi: max perturbation parameter for BCQ. Default to 0.05.
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For advanced usage (how to customize the network), please refer to
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:ref:`build_the_network`.
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.. seealso::
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You can refer to `examples/offline/offline_bcq.py` to see how to use it.
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"""
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def __init__(
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self,
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preprocess_net: nn.Module,
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max_action: float,
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device: str | int | torch.device = "cpu",
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phi: float = 0.05,
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):
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# preprocess_net: input_dim=state_dim+action_dim, output_dim=action_dim
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super().__init__()
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self.preprocess_net = preprocess_net
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self.device = device
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self.max_action = max_action
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self.phi = phi
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def forward(self, state: torch.Tensor, action: torch.Tensor) -> torch.Tensor:
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# preprocess_net
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logits = self.preprocess_net(torch.cat([state, action], -1))[0]
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noise = self.phi * self.max_action * torch.tanh(logits)
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# clip to [-max_action, max_action]
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return (noise + action).clamp(-self.max_action, self.max_action)
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class VAE(nn.Module):
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"""Implementation of VAE.
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It models the distribution of action. Given a state, it can generate actions similar to those in batch.
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It is used in BCQ algorithm.
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:param encoder: the encoder in VAE. Its input_dim must be
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state_dim + action_dim, and output_dim must be hidden_dim.
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:param decoder: the decoder in VAE. Its input_dim must be
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state_dim + latent_dim, and output_dim must be action_dim.
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:param hidden_dim: the size of the last linear-layer in encoder.
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:param latent_dim: the size of latent layer.
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:param max_action: the maximum value of each dimension of action.
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:param device: which device to create this model on.
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Default to "cpu".
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For advanced usage (how to customize the network), please refer to
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:ref:`build_the_network`.
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|
|
.. seealso::
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You can refer to `examples/offline/offline_bcq.py` to see how to use it.
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"""
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def __init__(
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self,
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encoder: nn.Module,
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decoder: nn.Module,
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hidden_dim: int,
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latent_dim: int,
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max_action: float,
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device: str | torch.device = "cpu",
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):
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super().__init__()
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self.encoder = encoder
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self.mean = nn.Linear(hidden_dim, latent_dim)
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self.log_std = nn.Linear(hidden_dim, latent_dim)
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self.decoder = decoder
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self.max_action = max_action
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self.latent_dim = latent_dim
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self.device = device
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def forward(
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self,
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state: torch.Tensor,
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action: torch.Tensor,
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) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
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# [state, action] -> z , [state, z] -> action
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latent_z = self.encoder(torch.cat([state, action], -1))
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# shape of z: (state.shape[:-1], hidden_dim)
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mean = self.mean(latent_z)
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# Clamped for numerical stability
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log_std = self.log_std(latent_z).clamp(-4, 15)
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std = torch.exp(log_std)
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# shape of mean, std: (state.shape[:-1], latent_dim)
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latent_z = mean + std * torch.randn_like(std) # (state.shape[:-1], latent_dim)
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reconstruction = self.decode(state, latent_z) # (state.shape[:-1], action_dim)
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return reconstruction, mean, std
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def decode(
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self,
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state: torch.Tensor,
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latent_z: torch.Tensor | None = None,
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) -> torch.Tensor:
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# decode(state) -> action
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if latent_z is None:
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# state.shape[0] may be batch_size
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# latent vector clipped to [-0.5, 0.5]
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latent_z = (
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torch.randn(state.shape[:-1] + (self.latent_dim,)).to(self.device).clamp(-0.5, 0.5)
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)
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# decode z with state!
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return self.max_action * torch.tanh(self.decoder(torch.cat([state, latent_z], -1)))
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