import torch
import numpy as np
from torch import nn
from tianshou.data import to_torch
[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,
max_action, device='cpu'):
super().__init__()
self.preprocess = preprocess_net
self.last = nn.Linear(128, np.prod(action_shape))
self._max = max_action
[docs] def forward(self, s, state=None, info={}):
logits, h = self.preprocess(s, state)
logits = self._max * torch.tanh(self.last(logits))
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, device='cpu'):
super().__init__()
self.device = device
self.preprocess = preprocess_net
self.last = nn.Linear(128, 1)
[docs] def forward(self, s, a=None, **kwargs):
s = to_torch(s, device=self.device, dtype=torch.float32)
s = s.flatten(1)
if a is not None:
a = to_torch(a, device=self.device, dtype=torch.float32)
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):
"""For advanced usage (how to customize the network), please refer to
:ref:`build_the_network`.
"""
def __init__(self, preprocess_net, action_shape,
max_action, device='cpu', unbounded=False):
super().__init__()
self.preprocess = preprocess_net
self.device = device
self.mu = nn.Linear(128, 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, state=None, **kwargs):
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), None
[docs]class RecurrentActorProb(nn.Module):
"""For advanced usage (how to customize the network), please refer to
:ref:`build_the_network`.
"""
def __init__(self, layer_num, state_shape, action_shape,
max_action, device='cpu'):
super().__init__()
self.device = device
self.nn = nn.LSTM(input_size=np.prod(state_shape), hidden_size=128,
num_layers=layer_num, batch_first=True)
self.mu = nn.Linear(128, np.prod(action_shape))
self.sigma = nn.Parameter(torch.zeros(np.prod(action_shape), 1))
[docs] def forward(self, s, **kwargs):
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)
logits, _ = self.nn(s)
logits = logits[:, -1]
mu = self.mu(logits)
shape = [1] * len(mu.shape)
shape[1] = -1
sigma = (self.sigma.view(shape) + torch.zeros_like(mu)).exp()
return (mu, sigma), None
[docs]class RecurrentCritic(nn.Module):
"""For advanced usage (how to customize the network), please refer to
:ref:`build_the_network`.
"""
def __init__(self, layer_num, state_shape, action_shape=0, device='cpu'):
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=128,
num_layers=layer_num, batch_first=True)
self.fc2 = nn.Linear(128 + np.prod(action_shape), 1)
[docs] def forward(self, s, a=None):
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:
if not isinstance(a, torch.Tensor):
a = torch.tensor(a, device=self.device, dtype=torch.float32)
s = torch.cat([s, a], dim=1)
s = self.fc2(s)
return s