Tianshou/examples/continuous_net.py
Minghao Zhang 77068af526
add examples, fix some bugs ()
* update atari.py

* fix setup.py
pass the pytest

* fix setup.py
pass the pytest

* add args "render"

* change the tensorboard writter

* change the tensorboard writter

* change device, render, tensorboard log location

* change device, render, tensorboard log location

* remove some wrong local files

* fix some tab mistakes and the envs name in continuous/test_xx.py

* add examples and point robot maze environment

* fix some bugs during testing examples

* add dqn network and fix some args

* change back the tensorboard writter's frequency to ensure ppo and a2c can write things normally

* add a warning to collector

* rm some unrelated files

* reformat

* fix a bug in test_dqn due to the model wrong selection
2020-03-28 07:27:18 +08:00

80 lines
2.8 KiB
Python

import torch
import numpy as np
from torch import nn
class Actor(nn.Module):
def __init__(self, layer_num, state_shape, action_shape,
max_action, device='cpu'):
super().__init__()
self.device = device
self.model = [
nn.Linear(np.prod(state_shape), 128),
nn.ReLU(inplace=True)]
for i in range(layer_num):
self.model += [nn.Linear(128, 128), nn.ReLU(inplace=True)]
self.model += [nn.Linear(128, np.prod(action_shape))]
self.model = nn.Sequential(*self.model)
self._max = max_action
def forward(self, s, **kwargs):
s = torch.tensor(s, device=self.device, dtype=torch.float)
batch = s.shape[0]
s = s.view(batch, -1)
logits = self.model(s)
logits = self._max * torch.tanh(logits)
return logits, None
class ActorProb(nn.Module):
def __init__(self, layer_num, state_shape, action_shape,
max_action, device='cpu'):
super().__init__()
self.device = device
self.model = [
nn.Linear(np.prod(state_shape), 128),
nn.ReLU(inplace=True)]
for i in range(layer_num):
self.model += [nn.Linear(128, 128), nn.ReLU(inplace=True)]
self.model = nn.Sequential(*self.model)
self.mu = nn.Linear(128, np.prod(action_shape))
self.sigma = nn.Linear(128, np.prod(action_shape))
self._max = max_action
def forward(self, s, **kwargs):
if not isinstance(s, torch.Tensor):
s = torch.tensor(s, device=self.device, dtype=torch.float)
batch = s.shape[0]
s = s.view(batch, -1)
logits = self.model(s)
mu = self._max * torch.tanh(self.mu(logits))
sigma = torch.exp(self.sigma(logits))
return (mu, sigma), None
class Critic(nn.Module):
def __init__(self, layer_num, state_shape, action_shape=0, device='cpu'):
super().__init__()
self.device = device
self.model = [
nn.Linear(np.prod(state_shape) + np.prod(action_shape), 128),
nn.ReLU(inplace=True)]
for i in range(layer_num):
self.model += [nn.Linear(128, 128), nn.ReLU(inplace=True)]
self.model += [nn.Linear(128, 1)]
self.model = nn.Sequential(*self.model)
def forward(self, s, a=None):
if not isinstance(s, torch.Tensor):
s = torch.tensor(s, device=self.device, dtype=torch.float)
if a is not None and not isinstance(a, torch.Tensor):
a = torch.tensor(a, device=self.device, dtype=torch.float)
batch = s.shape[0]
s = s.view(batch, -1)
if a is None:
logits = self.model(s)
else:
a = a.view(batch, -1)
logits = self.model(torch.cat([s, a], dim=1))
return logits