agents/qgdp.py [20:49]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
class Critic(nn.Module):
    def __init__(self, state_dim, action_dim, hidden_dim=256):
        super(Critic, self).__init__()
        self.q1_model = nn.Sequential(nn.Linear(state_dim + action_dim, hidden_dim),
                                      nn.Mish(),
                                      nn.Linear(hidden_dim, hidden_dim),
                                      nn.Mish(),
                                      nn.Linear(hidden_dim, hidden_dim),
                                      nn.Mish(),
                                      nn.Linear(hidden_dim, 1))

        self.q2_model = nn.Sequential(nn.Linear(state_dim + action_dim, hidden_dim),
                                      nn.Mish(),
                                      nn.Linear(hidden_dim, hidden_dim),
                                      nn.Mish(),
                                      nn.Linear(hidden_dim, hidden_dim),
                                      nn.Mish(),
                                      nn.Linear(hidden_dim, 1))

    def forward(self, state, action):
        x = torch.cat([state, action], dim=-1)
        return self.q1_model(x), self.q2_model(x)

    def q1(self, state, action):
        x = torch.cat([state, action], dim=-1)
        return self.q1_model(x)

    def q_min(self, state, action):
        q1, q2 = self.forward(state, action)
        return torch.min(q1, q2)
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -



agents/ql_cvae.py [61:90]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
class Critic(nn.Module):
    def __init__(self, state_dim, action_dim, hidden_dim=256):
        super(Critic, self).__init__()
        self.q1_model = nn.Sequential(nn.Linear(state_dim + action_dim, hidden_dim),
                                      nn.Mish(),
                                      nn.Linear(hidden_dim, hidden_dim),
                                      nn.Mish(),
                                      nn.Linear(hidden_dim, hidden_dim),
                                      nn.Mish(),
                                      nn.Linear(hidden_dim, 1))

        self.q2_model = nn.Sequential(nn.Linear(state_dim + action_dim, hidden_dim),
                                      nn.Mish(),
                                      nn.Linear(hidden_dim, hidden_dim),
                                      nn.Mish(),
                                      nn.Linear(hidden_dim, hidden_dim),
                                      nn.Mish(),
                                      nn.Linear(hidden_dim, 1))

    def forward(self, state, action):
        x = torch.cat([state, action], dim=-1)
        return self.q1_model(x), self.q2_model(x)

    def q1(self, state, action):
        x = torch.cat([state, action], dim=-1)
        return self.q1_model(x)

    def q_min(self, state, action):
        q1, q2 = self.forward(state, action)
        return torch.min(q1, q2)
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -