mirror of
https://github.com/wassname/DeepRL.git
synced 2026-09-09 11:13:47 +08:00
Major refactor
This commit is contained in:
Shangtong Zhang
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from conv_network import *
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from shallow_network import *
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from continuous_action_network import *
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#######################################################################
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# Copyright (C) 2017 Shangtong Zhang(zhangshangtong.cpp@gmail.com) #
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# Permission given to modify the code as long as you keep this #
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# declaration at the top #
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#######################################################################
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from network import *
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class ContinuousActorCriticNet(nn.Module, BasicNet):
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def __init__(self, state_dim, hidden_dim, action_dim):
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super(ContinuousActorCriticNet, self).__init__()
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hidden_size1 = 64
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hidden_size2 = 64
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self.fc1 = nn.Linear(state_dim, hidden_size1)
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self.fc2 = nn.Linear(hidden_size1, hidden_size2)
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self.fc_mean = nn.Linear(hidden_size2, action_dim)
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self.fc_var = nn.Linear(hidden_size2, action_dim)
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self.fc_critic = nn.Linear(hidden_size2, 1)
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BasicNet.__init__(self, None, False)
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def forward(self, x):
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x = self.to_torch_variable(x)
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x = x.view(x.size(0), -1)
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x = F.relu(self.fc1(x))
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phi = F.relu(self.fc2(x))
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return phi
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def predict(self, x):
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phi = self.forward(x)
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mean = self.fc_mean(phi)
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var = F.softplus(self.fc_var(phi) + 1e-5)
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value = self.fc_critic(phi)
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return mean, var, value
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def critic(self, x):
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phi = self.forward(x)
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return self.fc_critic(phi)
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class DDPGActorNet(nn.Module, BasicNet):
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def __init__(self,
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state_dim,
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action_dim,
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output_gate,
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gpu=False):
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super(DDPGActorNet, self).__init__()
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self.layer1 = nn.Linear(state_dim, 400)
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self.layer2 = nn.Linear(400, 300)
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self.layer3 = nn.Linear(300, action_dim)
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self.output_gate = output_gate
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BasicNet.__init__(self, None, False, False)
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self.init_weights()
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def init_weights(self):
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bound = 3e-3
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self.layer3.weight.data.uniform_(-bound, bound)
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# self.layer3.bias.data.uniform_(-bound, bound)
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def fanin(size):
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v = 1.0 / np.sqrt(size[1])
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return torch.FloatTensor(size).uniform_(-v, v)
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self.layer1.weight.data = fanin(self.layer1.weight.data.size())
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# self.layer1.bias.data = fanin(self.layer1.bias.data.size())
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self.layer2.weight.data = fanin(self.layer2.weight.data.size())
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# self.layer2.bias.data = fanin(self.layer2.bias.data.size())
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def forward(self, x):
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x = self.to_torch_variable(x)
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x = F.relu(self.layer1(x))
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x = F.relu(self.layer2(x))
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x = self.layer3(x)
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# x = self.output_gate(self.layer3(x))
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return x
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def predict(self, x, to_numpy=True):
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y = self.forward(x)
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if to_numpy:
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y = y.cpu().data.numpy()
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return y
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class DDPGCriticNet(nn.Module, BasicNet):
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def __init__(self,
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state_dim,
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action_dim,
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gpu=False):
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super(DDPGCriticNet, self).__init__()
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self.layer1 = nn.Linear(state_dim, 400)
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self.layer2 = nn.Linear(400 + action_dim, 300)
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self.layer3 = nn.Linear(300, 1)
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BasicNet.__init__(self, None, False, False)
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self.init_weights()
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def init_weights(self):
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bound = 3e-3
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self.layer3.weight.data.uniform_(-bound, bound)
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# self.layer3.bias.data.uniform_(-bound, bound)
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def fanin(size):
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v = 1.0 / np.sqrt(size[1])
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return torch.FloatTensor(size).uniform_(-v, v)
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self.layer1.weight.data = fanin(self.layer1.weight.data.size())
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# self.layer1.bias.data = fanin(self.layer1.bias.data.size())
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self.layer2.weight.data = fanin(self.layer2.weight.data.size())
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# self.layer2.bias.data = fanin(self.layer2.bias.data.size())
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def forward(self, x, action):
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x = self.to_torch_variable(x)
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action = self.to_torch_variable(action)
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x = F.relu(self.layer1(x))
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x = F.relu(self.layer2(torch.cat([x, action], dim=1)))
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x = self.layer3(x)
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return x
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def predict(self, x, action):
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return self.forward(x, action)
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@@ -0,0 +1,147 @@
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#######################################################################
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# Copyright (C) 2017 Shangtong Zhang(zhangshangtong.cpp@gmail.com) #
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# Permission given to modify the code as long as you keep this #
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# declaration at the top #
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#######################################################################
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from network import *
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# Network for pixel Atari game with value based methods
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class NatureConvNet(nn.Module, VanillaNet):
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def __init__(self, in_channels, n_actions, optimizer_fn=None, gpu=True):
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super(NatureConvNet, self).__init__()
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self.conv1 = nn.Conv2d(in_channels, 32, kernel_size=8, stride=4)
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self.conv2 = nn.Conv2d(32, 64, kernel_size=4, stride=2)
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self.conv3 = nn.Conv2d(64, 64, kernel_size=3, stride=1)
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self.fc4 = nn.Linear(7 * 7 * 64, 512)
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self.fc5 = nn.Linear(512, n_actions)
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self.criterion = nn.MSELoss()
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BasicNet.__init__(self, optimizer_fn, gpu)
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def forward(self, x):
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x = self.to_torch_variable(x)
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y = F.relu(self.conv1(x))
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y = F.relu(self.conv2(y))
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y = F.relu(self.conv3(y))
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y = y.view(y.size(0), -1)
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y = F.relu(self.fc4(y))
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return self.fc5(y)
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# Network for pixel Atari game with dueling architecture
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class DuelingNatureConvNet(nn.Module, DuelingNet):
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def __init__(self, in_channels, n_actions, optimizer_fn=None, gpu=True):
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super(DuelingNatureConvNet, self).__init__()
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self.conv1 = nn.Conv2d(in_channels, 32, kernel_size=8, stride=4)
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self.conv2 = nn.Conv2d(32, 64, kernel_size=4, stride=2)
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self.conv3 = nn.Conv2d(64, 64, kernel_size=3, stride=1)
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self.fc4 = nn.Linear(7 * 7 * 64, 512)
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self.fc_advantage = nn.Linear(512, n_actions)
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self.fc_value = nn.Linear(512, 1)
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self.criterion = nn.MSELoss()
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BasicNet.__init__(self, optimizer_fn, gpu)
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def forward(self, x):
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x = self.to_torch_variable(x)
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y = F.relu(self.conv1(x))
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y = F.relu(self.conv2(y))
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y = F.relu(self.conv3(y))
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y = y.view(y.size(0), -1)
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phi = F.relu(self.fc4(y))
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return phi
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# Network for pixel Atari game with actor critic
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class ActorCriticNatureConvNet(nn.Module, ActorCriticNet):
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def __init__(self,
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in_channels,
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n_actions,
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xentropy_weight=0.01,
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grad_threshold=40,
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gpu=True):
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super(ActorCriticNatureConvNet, self).__init__()
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self.conv1 = nn.Conv2d(in_channels, 32, kernel_size=8, stride=4)
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self.conv2 = nn.Conv2d(32, 64, kernel_size=4, stride=2)
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self.conv3 = nn.Conv2d(64, 64, kernel_size=3, stride=1)
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self.fc4 = nn.Linear(7 * 7 * 64, 512)
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self.fc_actor = nn.Linear(512, n_actions)
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self.fc_critic = nn.Linear(512, 1)
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self.xentropy_weight = xentropy_weight
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self.grad_threshold = grad_threshold
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BasicNet.__init__(self, optimizer_fn=None, gpu=gpu)
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def forward(self, x):
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x = self.to_torch_variable(x)
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y = F.elu(self.conv1(x))
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y = F.elu(self.conv2(y))
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y = F.elu(self.conv3(y))
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y = y.view(y.size(0), -1)
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return F.elu(self.fc4(y))
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class OpenAIActorCriticConvNet(nn.Module, ActorCriticNet):
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def __init__(self,
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in_channels,
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n_actions,
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LSTM=False):
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super(OpenAIActorCriticConvNet, self).__init__()
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self.conv1 = nn.Conv2d(in_channels, 32, 3, stride=2, padding=1)
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self.conv2 = nn.Conv2d(32, 32, 3, stride=2, padding=1)
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self.conv3 = nn.Conv2d(32, 32, 3, stride=2, padding=1)
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self.conv4 = nn.Conv2d(32, 32, 3, stride=2, padding=1)
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self.LSTM = LSTM
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hidden_units = 256
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if LSTM:
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self.layer5 = nn.LSTMCell(32 * 3 * 3, hidden_units)
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else:
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self.layer5 = nn.Linear(32 * 3 * 3, hidden_units)
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self.fc_actor = nn.Linear(hidden_units, n_actions)
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self.fc_critic = nn.Linear(hidden_units, 1)
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BasicNet.__init__(self, optimizer_fn=None, gpu=False, LSTM=LSTM)
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if LSTM:
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self.h = self.to_torch_variable(np.zeros((1, hidden_units)))
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self.c = self.to_torch_variable(np.zeros((1, hidden_units)))
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def forward(self, x, update_LSTM=True):
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x = self.to_torch_variable(x)
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y = F.elu(self.conv1(x))
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y = F.elu(self.conv2(y))
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y = F.elu(self.conv3(y))
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y = F.elu(self.conv4(y))
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y = y.view(y.size(0), -1)
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if self.LSTM:
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h, c = self.layer5(y, (self.h, self.c))
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if update_LSTM:
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self.h = h
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self.c = c
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phi = h
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else:
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phi = F.elu(self.layer5(y))
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return phi
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class OpenAIConvNet(nn.Module, VanillaNet):
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def __init__(self,
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in_channels,
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n_actions):
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super(OpenAIConvNet, self).__init__()
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self.conv1 = nn.Conv2d(in_channels, 32, 3, stride=2, padding=1)
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self.conv2 = nn.Conv2d(32, 32, 3, stride=2, padding=1)
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self.conv3 = nn.Conv2d(32, 32, 3, stride=2, padding=1)
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self.conv4 = nn.Conv2d(32, 32, 3, stride=2, padding=1)
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hidden_units = 256
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self.layer5 = nn.Linear(32 * 3 * 3, hidden_units)
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self.fc6 = nn.Linear(hidden_units, n_actions)
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BasicNet.__init__(self, optimizer_fn=None, gpu=False, LSTM=False)
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def forward(self, x, update_LSTM=True):
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x = self.to_torch_variable(x)
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y = F.elu(self.conv1(x))
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y = F.elu(self.conv2(y))
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y = F.elu(self.conv3(y))
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y = F.elu(self.conv4(y))
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y = y.view(y.size(0), -1)
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phi = F.elu(self.layer5(y))
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return self.fc6(phi)
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@@ -0,0 +1,72 @@
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#######################################################################
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# Copyright (C) 2017 Shangtong Zhang(zhangshangtong.cpp@gmail.com) #
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# Permission given to modify the code as long as you keep this #
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# declaration at the top #
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#######################################################################
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import torch
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from torch.autograd import Variable
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import torch.nn as nn
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import torch.nn.functional as F
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import numpy as np
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# Base class for all kinds of network
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class BasicNet:
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def __init__(self, optimizer_fn, gpu, LSTM=False):
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if optimizer_fn is not None:
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self.optimizer = optimizer_fn(self.parameters())
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self.gpu = gpu and torch.cuda.is_available()
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self.LSTM = LSTM
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if self.gpu:
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self.cuda()
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def to_torch_variable(self, x, dtype='float32'):
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if isinstance(x, Variable):
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return x
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if not isinstance(x, torch.FloatTensor):
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x = torch.from_numpy(np.asarray(x, dtype=dtype))
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if self.gpu:
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x = x.cuda()
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return Variable(x)
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def reset(self, terminal):
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if not self.LSTM:
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return
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if terminal:
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self.h.data.zero_()
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self.c.data.zero_()
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self.h = Variable(self.h.data)
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self.c = Variable(self.c.data)
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# Base class for value based methods
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class VanillaNet(BasicNet):
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def predict(self, x, to_numpy=False):
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y = self.forward(x)
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if to_numpy:
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y = y.cpu().data.numpy()
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return y
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# Base class for actor critic method
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class ActorCriticNet(BasicNet):
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def predict(self, x):
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phi = self.forward(x, True)
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pre_prob = self.fc_actor(phi)
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prob = F.softmax(pre_prob)
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log_prob = F.log_softmax(pre_prob)
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value = self.fc_critic(phi)
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return prob, log_prob, value
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def critic(self, x):
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phi = self.forward(x, False)
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return self.fc_critic(phi)
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# Base class for dueling architecture
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class DuelingNet(BasicNet):
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def predict(self, x, to_numpy=False):
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phi = self.forward(x)
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value = self.fc_value(phi)
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advantange = self.fc_advantage(phi)
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q = value.expand_as(advantange) + (advantange - advantange.mean(1).expand_as(advantange))
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if to_numpy:
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return q.cpu().data.numpy()
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return q
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@@ -0,0 +1,64 @@
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#######################################################################
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# Copyright (C) 2017 Shangtong Zhang(zhangshangtong.cpp@gmail.com) #
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# Permission given to modify the code as long as you keep this #
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# declaration at the top #
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#######################################################################
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from network import *
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# Network for CartPole with value based methods
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class FCNet(nn.Module, VanillaNet):
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def __init__(self, dims, optimizer_fn=None, gpu=True):
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super(FCNet, self).__init__()
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self.fc1 = nn.Linear(dims[0], dims[1])
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self.fc2 = nn.Linear(dims[1], dims[2])
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self.fc3 = nn.Linear(dims[2], dims[3])
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self.criterion = nn.MSELoss()
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BasicNet.__init__(self, optimizer_fn, gpu)
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def forward(self, x):
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x = self.to_torch_variable(x)
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x = x.view(x.size(0), -1)
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y = F.relu(self.fc1(x))
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y = F.relu(self.fc2(y))
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y = self.fc3(y)
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return y
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# Network for CartPole with dueling architecture
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class DuelingFCNet(nn.Module, DuelingNet):
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def __init__(self, dims, optimizer_fn=None, gpu=True):
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super(DuelingFCNet, self).__init__()
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self.fc1 = nn.Linear(dims[0], dims[1])
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self.fc2 = nn.Linear(dims[1], dims[2])
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self.fc_value = nn.Linear(dims[2], 1)
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self.fc_advantage = nn.Linear(dims[2], dims[3])
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self.criterion = nn.MSELoss()
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BasicNet.__init__(self, optimizer_fn, gpu)
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def forward(self, x):
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x = self.to_torch_variable(x)
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x = x.view(x.size(0), -1)
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y = F.relu(self.fc1(x))
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phi = F.relu(self.fc2(y))
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return phi
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# Network for CartPole with actor critic
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class ActorCriticFCNet(nn.Module, ActorCriticNet):
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def __init__(self, state_dim, action_dim):
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super(ActorCriticFCNet, self).__init__()
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hidden_size1 = 50
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hidden_size2 = 200
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self.fc1 = nn.Linear(state_dim, hidden_size1)
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self.fc2 = nn.Linear(hidden_size1, hidden_size2)
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self.fc_actor = nn.Linear(hidden_size2, action_dim)
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self.fc_critic = nn.Linear(hidden_size2, 1)
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BasicNet.__init__(self, None, False)
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def forward(self, x, update_LSTM=True):
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x = self.to_torch_variable(x)
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x = x.view(x.size(0), -1)
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x = F.relu(self.fc1(x))
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phi = self.fc2(x)
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return phi
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