77068af526
* 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
81 lines
2.7 KiB
Python
81 lines
2.7 KiB
Python
import torch
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import numpy as np
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from torch import nn
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import torch.nn.functional as F
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class Net(nn.Module):
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def __init__(self, layer_num, state_shape, action_shape=0, device='cpu'):
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super().__init__()
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self.device = device
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self.model = [
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nn.Linear(np.prod(state_shape), 128),
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nn.ReLU(inplace=True)]
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for i in range(layer_num):
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self.model += [nn.Linear(128, 128), nn.ReLU(inplace=True)]
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if action_shape:
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self.model += [nn.Linear(128, np.prod(action_shape))]
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self.model = nn.Sequential(*self.model)
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def forward(self, s, state=None, info={}):
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if not isinstance(s, torch.Tensor):
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s = torch.tensor(s, device=self.device, dtype=torch.float)
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batch = s.shape[0]
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s = s.view(batch, -1)
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logits = self.model(s)
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return logits, state
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class Actor(nn.Module):
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def __init__(self, preprocess_net, action_shape):
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super().__init__()
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self.preprocess = preprocess_net
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self.last = nn.Linear(128, np.prod(action_shape))
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def forward(self, s, state=None, info={}):
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logits, h = self.preprocess(s, state)
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logits = F.softmax(self.last(logits), dim=-1)
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return logits, h
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class Critic(nn.Module):
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def __init__(self, preprocess_net):
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super().__init__()
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self.preprocess = preprocess_net
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self.last = nn.Linear(128, 1)
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def forward(self, s):
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logits, h = self.preprocess(s, None)
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logits = self.last(logits)
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return logits
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class DQN(nn.Module):
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def __init__(self, h, w, action_shape, device='cpu'):
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super(DQN, self).__init__()
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self.device = device
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self.conv1 = nn.Conv2d(3, 16, kernel_size=5, stride=2)
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self.bn1 = nn.BatchNorm2d(16)
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self.conv2 = nn.Conv2d(16, 32, kernel_size=5, stride=2)
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self.bn2 = nn.BatchNorm2d(32)
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self.conv3 = nn.Conv2d(32, 32, kernel_size=5, stride=2)
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self.bn3 = nn.BatchNorm2d(32)
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def conv2d_size_out(size, kernel_size=5, stride=2):
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return (size - (kernel_size - 1) - 1) // stride + 1
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convw = conv2d_size_out(conv2d_size_out(conv2d_size_out(w)))
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convh = conv2d_size_out(conv2d_size_out(conv2d_size_out(h)))
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linear_input_size = convw * convh * 32
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self.head = nn.Linear(linear_input_size, action_shape)
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def forward(self, x, state=None, info={}):
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if not isinstance(x, torch.Tensor):
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s = torch.tensor(x, device=self.device, dtype=torch.float)
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x = F.relu(self.bn1(self.conv1(x)))
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x = F.relu(self.bn2(self.conv2(x)))
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x = F.relu(self.bn3(self.conv3(x)))
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return self.head(x.view(x.size(0), -1)), state
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