import torch
import numpy as np
from torch import nn
from typing import Tuple, Union, Optional
from tianshou.data import to_torch
[docs]def miniblock(inp: int, oup: int, norm_layer: nn.modules.Module):
ret = [nn.Linear(inp, oup)]
if norm_layer is not None:
ret += [norm_layer(oup)]
ret += [nn.ReLU(inplace=True)]
return ret
[docs]class Net(nn.Module):
"""Simple MLP backbone. For advanced usage (how to customize the network),
please refer to :ref:`build_the_network`.
:param bool 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.
:param bool dueling: whether to use dueling network to calculate Q values
(for Dueling DQN), defaults to False.
:param nn.modules.Module norm_layer: use which normalization before ReLU,
e.g., ``nn.LayerNorm`` and ``nn.BatchNorm1d``, defaults to None.
"""
def __init__(self, layer_num: int, state_shape: tuple,
action_shape: Optional[tuple] = 0,
device: Union[str, torch.device] = 'cpu',
softmax: bool = False,
concat: bool = False,
hidden_layer_size: int = 128,
dueling: Optional[Tuple[int, int]] = None,
norm_layer: Optional[nn.modules.Module] = None):
super().__init__()
self.device = device
self.dueling = dueling
self.softmax = softmax
input_size = np.prod(state_shape)
if concat:
input_size += np.prod(action_shape)
self.model = miniblock(input_size, hidden_layer_size, norm_layer)
for i in range(layer_num):
self.model += miniblock(hidden_layer_size,
hidden_layer_size, norm_layer)
if self.dueling is None:
if action_shape and not concat:
self.model += [nn.Linear(hidden_layer_size,
np.prod(action_shape))]
else: # dueling DQN
assert isinstance(self.dueling, tuple) and len(self.dueling) == 2
q_layer_num, v_layer_num = self.dueling
self.Q, self.V = [], []
for i in range(q_layer_num):
self.Q += miniblock(hidden_layer_size,
hidden_layer_size, norm_layer)
for i in range(v_layer_num):
self.V += miniblock(hidden_layer_size,
hidden_layer_size, norm_layer)
if action_shape and not concat:
self.Q += [nn.Linear(hidden_layer_size, np.prod(action_shape))]
self.V += [nn.Linear(hidden_layer_size, 1)]
self.Q = nn.Sequential(*self.Q)
self.V = nn.Sequential(*self.V)
self.model = nn.Sequential(*self.model)
[docs] def forward(self, s, state=None, info={}):
"""s -> flatten -> logits"""
s = to_torch(s, device=self.device, dtype=torch.float32)
s = s.reshape(s.size(0), -1)
logits = self.model(s)
if self.dueling is not None: # Dueling DQN
q, v = self.Q(logits), self.V(logits)
logits = q - q.mean(dim=1, keepdim=True) + v
if self.softmax:
logits = torch.softmax(logits, dim=-1)
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', hidden_layer_size=128):
super().__init__()
self.state_shape = state_shape
self.action_shape = action_shape
self.device = device
self.nn = nn.LSTM(input_size=hidden_layer_size,
hidden_size=hidden_layer_size,
num_layers=layer_num, batch_first=True)
self.fc1 = nn.Linear(np.prod(state_shape), hidden_layer_size)
self.fc2 = nn.Linear(hidden_layer_size, np.prod(action_shape))
[docs] def forward(self, s, state=None, info={}):
"""In the evaluation mode, s should be with shape ``[bsz, dim]``; in
the training mode, s should be with shape ``[bsz, len, dim]``. See the
code and comment for more detail.
"""
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()}