def __init__(self, state_size, action_size, H=64):
super().__init__()
self.feature = nn.Linear(state_size, H)
self.advantage_n1 = NoisyLinear(H, H)
self.advantage_n2 = NoisyLinear(H, action_size)
self.value_n1 = NoisyLinear(H, H)
self.value_n2 = NoisyLinear(H, 1)
class NoisyDuelingRainbowNetwork(nn.Module):
def __init__(self, state_size, action_size, H=64):
super().__init__()
self.feature = nn.Linear(state_size, H)
self.advantage_n1 = NoisyLinear(H, H)
self.advantage_n2 = NoisyLinear(H, action_size)
self.value_n1 = NoisyLinear(H, H)
self.value_n2 = NoisyLinear(H, 1)
def forward(self, state, log=False):
features = F.relu(self.feature(state))
advantage = F.relu(self.advantage_n1(features))
advantage = self.advantage_n2(advantage)
value = F.relu(self.value_n1(features))
value = self.value_n2(value)
q = value + (advantage - advantage.mean())
return q
def reset_noise(self):
self.advantage_n1.reset_noise()
self.advantage_n2.reset_noise()
self.value_n1.reset_noise()
self.value_n2.reset_noise()
class NoisyRainbowNetwork(nn.Module):
def __init__(self, state_size, action_size, H=64):
super().__init__()
self.linear = nn.Linear(state_size, H)
self.n2 = NoisyLinear(H, H)
self.n3 = NoisyLinear(H, action_size)
def forward(self, state):
x = F.relu(self.linear(state))
x = F.relu(self.n2(x))
return self.n3(x)
def reset_noise(self):
self.n2.reset_noise()
self.n3.reset_noise()
def __init__(self, state_size, action_size, atoms, H=64):
super(QRRainbowNetwork, self).__init__()
self.action_size = action_size
self.state_size = state_size
self.atoms = atoms
self.feature = nn.Linear(state_size, H)
self.advantage_n1 = NoisyLinear(H, H)
self.advantage_n2 = NoisyLinear(H, action_size*atoms)
self.value_n1 = NoisyLinear(H, H)
self.value_n2 = NoisyLinear(H, 1*atoms)
class RainbowNetwork(nn.Module):
def __init__(self, state_size, action_size, atoms, H=64):
super(RainbowNetwork, self).__init__()
self.action_size = action_size
self.state_size = state_size
self.atoms = atoms
self.feature = nn.Linear(state_size, H)
self.advantage_n1 = NoisyLinear(H, H)
self.advantage_n2 = NoisyLinear(H, action_size*atoms)
self.value_n1 = NoisyLinear(H, H)
self.value_n2 = NoisyLinear(H, 1*atoms)
def forward(self, state, log=False):
features = F.relu(self.feature(state))
advantage = F.relu(self.advantage_n1(features))
advantage = self.advantage_n2(advantage)
value = F.relu(self.value_n1(features))
value = self.value_n2(value)
advantage = advantage.view(-1, self.action_size, self.atoms)
value = value.view(-1, 1, self.atoms)
q = value + (advantage - advantage.mean(1, keepdim=True))
if log:
q = F.log_softmax(q, dim=2)
else:
q = F.softmax(q, dim=2)
return q
def reset_noise(self):
self.advantage_n1.reset_noise()
self.advantage_n2.reset_noise()
self.value_n1.reset_noise()
self.value_n2.reset_noise()
def __init__(self, state_size, action_size, H=64):
super().__init__()
self.linear = nn.Linear(state_size, H)
self.n2 = NoisyLinear(H, H)
self.n3 = NoisyLinear(H, action_size)