import torch
import numpy as np
from torch import nn
import torch.nn.functional as F
from typing import Any, Dict, Tuple, Union, Optional, Sequence
from tianshou.data import to_torch
[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],
hidden_layer_size: int = 128,
softmax_output: bool = True,
) -> None:
super().__init__()
self.preprocess = preprocess_net
self.last = nn.Linear(hidden_layer_size, np.prod(action_shape))
self.softmax_output = softmax_output
[docs] def forward(
self,
s: Union[np.ndarray, torch.Tensor],
state: Optional[Any] = None,
info: Dict[str, Any] = {},
) -> Tuple[torch.Tensor, Any]:
r"""Mapping: s -> Q(s, \*)."""
logits, h = self.preprocess(s, state)
logits = self.last(logits)
if self.softmax_output:
logits = F.softmax(logits, dim=-1)
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,
hidden_layer_size: int = 128,
last_size: int = 1
) -> None:
super().__init__()
self.preprocess = preprocess_net
self.last = nn.Linear(hidden_layer_size, last_size)
[docs] def forward(
self, s: Union[np.ndarray, torch.Tensor], **kwargs: Any
) -> torch.Tensor:
"""Mapping: s -> V(s)."""
logits, h = self.preprocess(s, state=kwargs.get("state", None))
logits = self.last(logits)
return logits
[docs]class DQN(nn.Module):
"""Reference: Human-level control through deep reinforcement learning.
For advanced usage (how to customize the network), please refer to
:ref:`build_the_network`.
"""
def __init__(
self,
c: int,
h: int,
w: int,
action_shape: Sequence[int],
device: Union[str, int, torch.device] = "cpu",
) -> None:
super().__init__()
self.device = device
def conv2d_size_out(
size: int, kernel_size: int = 5, stride: int = 2
) -> int:
return (size - (kernel_size - 1) - 1) // stride + 1
def conv2d_layers_size_out(
size: int,
kernel_size_1: int = 8,
stride_1: int = 4,
kernel_size_2: int = 4,
stride_2: int = 2,
kernel_size_3: int = 3,
stride_3: int = 1,
) -> int:
size = conv2d_size_out(size, kernel_size_1, stride_1)
size = conv2d_size_out(size, kernel_size_2, stride_2)
size = conv2d_size_out(size, kernel_size_3, stride_3)
return size
convw = conv2d_layers_size_out(w)
convh = conv2d_layers_size_out(h)
linear_input_size = convw * convh * 64
self.net = nn.Sequential(
nn.Conv2d(c, 32, kernel_size=8, stride=4),
nn.ReLU(inplace=True),
nn.Conv2d(32, 64, kernel_size=4, stride=2),
nn.ReLU(inplace=True),
nn.Conv2d(64, 64, kernel_size=3, stride=1),
nn.ReLU(inplace=True),
nn.Flatten(),
nn.Linear(linear_input_size, 512),
nn.ReLU(inplace=True),
nn.Linear(512, np.prod(action_shape)),
)
[docs] def forward(
self,
x: Union[np.ndarray, torch.Tensor],
state: Optional[Any] = None,
info: Dict[str, Any] = {},
) -> Tuple[torch.Tensor, Any]:
r"""Mapping: x -> Q(x, \*)."""
if not isinstance(x, torch.Tensor):
x = to_torch(x, device=self.device, dtype=torch.float32)
return self.net(x), state