import torch
import numpy as np
from torch import nn
from typing import Any, Dict, Tuple, Union, Optional, Sequence
from tianshou.data import to_torch, to_torch_as
[docs]class Actor(nn.Module):
"""Simple actor network with MLP.
For advanced usage (how to customize the network), please refer to
:ref:`build_the_network`.
"""
def __init__(
self,
preprocess_net: nn.Module,
action_shape: Sequence[int],
max_action: float = 1.0,
device: Union[str, int, torch.device] = "cpu",
hidden_layer_size: int = 128,
) -> None:
super().__init__()
self.preprocess = preprocess_net
self.last = nn.Linear(hidden_layer_size, np.prod(action_shape))
self._max = max_action
[docs] def forward(
self,
s: Union[np.ndarray, torch.Tensor],
state: Optional[Any] = None,
info: Dict[str, Any] = {},
) -> Tuple[torch.Tensor, Any]:
"""Mapping: s -> logits -> action."""
logits, h = self.preprocess(s, state)
logits = self._max * torch.tanh(self.last(logits))
return logits, h
[docs]class Critic(nn.Module):
"""Simple critic network with MLP.
For advanced usage (how to customize the network), please refer to
:ref:`build_the_network`.
"""
def __init__(
self,
preprocess_net: nn.Module,
device: Union[str, int, torch.device] = "cpu",
hidden_layer_size: int = 128,
) -> None:
super().__init__()
self.device = device
self.preprocess = preprocess_net
self.last = nn.Linear(hidden_layer_size, 1)
[docs] def forward(
self,
s: Union[np.ndarray, torch.Tensor],
a: Optional[Union[np.ndarray, torch.Tensor]] = None,
info: Dict[str, Any] = {},
) -> torch.Tensor:
"""Mapping: (s, a) -> logits -> Q(s, a)."""
s = to_torch(s, device=self.device, dtype=torch.float32)
s = s.flatten(1)
if a is not None:
a = to_torch_as(a, s)
a = a.flatten(1)
s = torch.cat([s, a], dim=1)
logits, h = self.preprocess(s)
logits = self.last(logits)
return logits
[docs]class ActorProb(nn.Module):
"""Simple actor network (output with a Gauss distribution) with MLP.
For advanced usage (how to customize the network), please refer to
:ref:`build_the_network`.
"""
def __init__(
self,
preprocess_net: nn.Module,
action_shape: Sequence[int],
max_action: float = 1.0,
device: Union[str, int, torch.device] = "cpu",
unbounded: bool = False,
hidden_layer_size: int = 128,
) -> None:
super().__init__()
self.preprocess = preprocess_net
self.device = device
self.mu = nn.Linear(hidden_layer_size, np.prod(action_shape))
self.sigma = nn.Parameter(torch.zeros(np.prod(action_shape), 1))
self._max = max_action
self._unbounded = unbounded
[docs] def forward(
self,
s: Union[np.ndarray, torch.Tensor],
state: Optional[Any] = None,
info: Dict[str, Any] = {},
) -> Tuple[Tuple[torch.Tensor, torch.Tensor], Any]:
"""Mapping: s -> logits -> (mu, sigma)."""
logits, h = self.preprocess(s, state)
mu = self.mu(logits)
if not self._unbounded:
mu = self._max * torch.tanh(mu)
shape = [1] * len(mu.shape)
shape[1] = -1
sigma = (self.sigma.view(shape) + torch.zeros_like(mu)).exp()
return (mu, sigma), state
[docs]class RecurrentActorProb(nn.Module):
"""Recurrent version of ActorProb.
For advanced usage (how to customize the network), please refer to
:ref:`build_the_network`.
"""
def __init__(
self,
layer_num: int,
state_shape: Sequence[int],
action_shape: Sequence[int],
max_action: float = 1.0,
device: Union[str, int, torch.device] = "cpu",
unbounded: bool = False,
hidden_layer_size: int = 128,
) -> None:
super().__init__()
self.device = device
self.nn = nn.LSTM(
input_size=np.prod(state_shape),
hidden_size=hidden_layer_size,
num_layers=layer_num,
batch_first=True,
)
self.mu = nn.Linear(hidden_layer_size, np.prod(action_shape))
self.sigma = nn.Parameter(torch.zeros(np.prod(action_shape), 1))
self._max = max_action
self._unbounded = unbounded
[docs] def forward(
self,
s: Union[np.ndarray, torch.Tensor],
state: Optional[Dict[str, torch.Tensor]] = None,
info: Dict[str, Any] = {},
) -> Tuple[Tuple[torch.Tensor, torch.Tensor], Dict[str, torch.Tensor]]:
"""Almost the same as :class:`~tianshou.utils.net.common.Recurrent`."""
s = to_torch(s, device=self.device, dtype=torch.float32)
# s [bsz, len, dim] (training) or [bsz, dim] (evaluation)
# In short, the tensor's shape in training phase is longer than which
# in evaluation phase.
if len(s.shape) == 2:
s = s.unsqueeze(-2)
self.nn.flatten_parameters()
if state is None:
s, (h, c) = self.nn(s)
else:
# we store the stack data in [bsz, len, ...] format
# but pytorch rnn needs [len, bsz, ...]
s, (h, c) = self.nn(s, (state["h"].transpose(0, 1).contiguous(),
state["c"].transpose(0, 1).contiguous()))
logits = s[:, -1]
mu = self.mu(logits)
if not self._unbounded:
mu = self._max * torch.tanh(mu)
shape = [1] * len(mu.shape)
shape[1] = -1
sigma = (self.sigma.view(shape) + torch.zeros_like(mu)).exp()
# please ensure the first dim is batch size: [bsz, len, ...]
return (mu, sigma), {"h": h.transpose(0, 1).detach(),
"c": c.transpose(0, 1).detach()}
[docs]class RecurrentCritic(nn.Module):
"""Recurrent version of Critic.
For advanced usage (how to customize the network), please refer to
:ref:`build_the_network`.
"""
def __init__(
self,
layer_num: int,
state_shape: Sequence[int],
action_shape: Sequence[int] = [0],
device: Union[str, int, torch.device] = "cpu",
hidden_layer_size: int = 128,
) -> None:
super().__init__()
self.state_shape = state_shape
self.action_shape = action_shape
self.device = device
self.nn = nn.LSTM(
input_size=np.prod(state_shape),
hidden_size=hidden_layer_size,
num_layers=layer_num,
batch_first=True,
)
self.fc2 = nn.Linear(hidden_layer_size + np.prod(action_shape), 1)
[docs] def forward(
self,
s: Union[np.ndarray, torch.Tensor],
a: Optional[Union[np.ndarray, torch.Tensor]] = None,
info: Dict[str, Any] = {},
) -> torch.Tensor:
"""Almost the same as :class:`~tianshou.utils.net.common.Recurrent`."""
s = to_torch(s, device=self.device, dtype=torch.float32)
# s [bsz, len, dim] (training) or [bsz, dim] (evaluation)
# In short, the tensor's shape in training phase is longer than which
# in evaluation phase.
assert len(s.shape) == 3
self.nn.flatten_parameters()
s, (h, c) = self.nn(s)
s = s[:, -1]
if a is not None:
a = to_torch_as(a, s)
s = torch.cat([s, a], dim=1)
s = self.fc2(s)
return s