import numpy as np
import torch
from torch import nn
from tianshou.data import to_torch
[docs]class Net(nn.Module):
"""Simple MLP backbone. For advanced usage (how to customize the network),
please refer to :ref:`build_the_network`.
:param concat: whether the input shape is concatenated by state_shape
and action_shape. If it is True, ``action_shape`` is not the output
shape, but affects the input shape.
"""
def __init__(self, layer_num, state_shape, action_shape=0, device='cpu',
softmax=False, concat=False):
super().__init__()
self.device = device
input_size = np.prod(state_shape)
if concat:
input_size += np.prod(action_shape)
self.model = [
nn.Linear(input_size, 128),
nn.ReLU(inplace=True)]
for i in range(layer_num):
self.model += [nn.Linear(128, 128), nn.ReLU(inplace=True)]
if action_shape and not concat:
self.model += [nn.Linear(128, np.prod(action_shape))]
if softmax:
self.model += [nn.Softmax(dim=-1)]
self.model = nn.Sequential(*self.model)
[docs] def forward(self, s, state=None, info={}):
s = to_torch(s, device=self.device, dtype=torch.float32)
s = s.flatten(1)
logits = self.model(s)
return logits, state
[docs]class Recurrent(nn.Module):
"""Simple Recurrent network based on LSTM. For advanced usage (how to
customize the network), please refer to :ref:`build_the_network`.
"""
def __init__(self, layer_num, state_shape, action_shape, device='cpu'):
super().__init__()
self.state_shape = state_shape
self.action_shape = action_shape
self.device = device
self.nn = nn.LSTM(input_size=128, hidden_size=128,
num_layers=layer_num, batch_first=True)
self.fc1 = nn.Linear(np.prod(state_shape), 128)
self.fc2 = nn.Linear(128, np.prod(action_shape))
[docs] def forward(self, s, state=None, info={}):
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)
s = self.fc1(s)
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()))
s = self.fc2(s[:, -1])
# please ensure the first dim is batch size: [bsz, len, ...]
return s, {'h': h.transpose(0, 1).detach(),
'c': c.transpose(0, 1).detach()}