import torch
import numpy as np
from torch import nn
import torch.nn.functional as F
[docs]class Actor(nn.Module):
"""For advanced usage (how to customize the network), please refer to
:ref:`build_the_network`.
"""
def __init__(self, preprocess_net, action_shape, hidden_layer_size=128):
super().__init__()
self.preprocess = preprocess_net
self.last = nn.Linear(hidden_layer_size, np.prod(action_shape))
[docs] def forward(self, s, state=None, info={}):
r"""s -> Q(s, \*)"""
logits, h = self.preprocess(s, state)
logits = F.softmax(self.last(logits), dim=-1)
return logits, h
[docs]class Critic(nn.Module):
"""For advanced usage (how to customize the network), please refer to
:ref:`build_the_network`.
"""
def __init__(self, preprocess_net, hidden_layer_size=128):
super().__init__()
self.preprocess = preprocess_net
self.last = nn.Linear(hidden_layer_size, 1)
[docs] def forward(self, s, **kwargs):
"""s -> V(s)"""
logits, h = self.preprocess(s, state=kwargs.get('state', None))
logits = self.last(logits)
return logits
[docs]class DQN(nn.Module):
"""For advanced usage (how to customize the network), please refer to
:ref:`build_the_network`.
Reference paper: "Human-level control through deep reinforcement learning".
"""
def __init__(self, h, w, action_shape, device='cpu'):
super(DQN, self).__init__()
self.device = device
def conv2d_size_out(size, kernel_size=5, stride=2):
return (size - (kernel_size - 1) - 1) // stride + 1
def conv2d_layers_size_out(size,
kernel_size_1=8, stride_1=4,
kernel_size_2=4, stride_2=2,
kernel_size_3=3, stride_3=1):
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(4, 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.Linear(512, action_shape)
)
[docs] def forward(self, x, state=None, info={}):
r"""x -> Q(x, \*)"""
if not isinstance(x, torch.Tensor):
x = torch.tensor(x, device=self.device, dtype=torch.float32)
x = x.permute(0, 3, 1, 2)
return self.net(x), state