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Update DDPG

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Shangtong Zhang
2018-01-19 11:24:32 -07:00
parent db49a76c53
commit eb16eeb2a1
3 changed files with 20 additions and 29 deletions
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network/continuous_action_network.py
+14 -22
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@@ -29,22 +29,18 @@ class DeterministicActorNet(nn.Module, BasicNet):
self.layer2 = nn.Linear(hidden_size, hidden_size)
self.batch_norm = batch_norm
BasicNet.__init__(self, None, gpu, False)
self.init_weights()
BasicNet.__init__(self, None, gpu, False)
def init_weights(self):
bound = 3e-3
self.layer3.weight.data.uniform_(-bound, bound)
self.layer3.bias.data.fill_(0)
nn.init.uniform(self.layer3.weight.data, -bound, bound)
nn.init.constant(self.layer3.bias.data, 0)
def fanin(size):
v = 1.0 / np.sqrt(size[1])
return torch.FloatTensor(size).uniform_(-v, v)
self.layer1.weight.data = fanin(self.layer1.weight.data.size())
self.layer1.bias.data.fill_(0)
self.layer2.weight.data = fanin(self.layer2.weight.data.size())
self.layer2.bias.data.fill_(0)
nn.init.xavier_uniform(self.layer1.weight.data)
nn.init.constant(self.layer1.bias.data, 0)
nn.init.xavier_uniform(self.layer2.weight.data)
nn.init.constant(self.layer2.bias.data, 0)
def forward(self, x):
x = self.to_torch_variable(x)
@@ -83,22 +79,18 @@ class DeterministicCriticNet(nn.Module, BasicNet):
self.bn2 = nn.BatchNorm1d(hidden_size)
self.batch_norm = batch_norm
BasicNet.__init__(self, None, gpu, False)
self.init_weights()
BasicNet.__init__(self, None, gpu, False)
def init_weights(self):
bound = 3e-3
self.layer3.weight.data.uniform_(-bound, bound)
self.layer3.bias.data.fill_(0)
nn.init.uniform(self.layer3.weight.data, -bound, bound)
nn.init.constant(self.layer3.bias.data, 0)
def fanin(size):
v = 1.0 / np.sqrt(size[1])
return torch.FloatTensor(size).uniform_(-v, v)
self.layer1.weight.data = fanin(self.layer1.weight.data.size())
self.layer1.bias.data.fill_(0)
self.layer2.weight.data = fanin(self.layer2.weight.data.size())
self.layer2.bias.data.fill_(0)
nn.init.xavier_uniform(self.layer1.weight.data)
nn.init.constant(self.layer1.bias.data, 0)
nn.init.xavier_uniform(self.layer2.weight.data)
nn.init.constant(self.layer2.bias.data, 0)
def forward(self, x, action):
x = self.to_torch_variable(x)