update rainbowdqn
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@@ -32,10 +32,10 @@ class MLP(nn.Module):
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return self.fc3(x)
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class Critic(nn.Module):
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def __init__(self, n_obs, action_dim, hidden_size, init_w=3e-3):
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def __init__(self, n_obs, n_actions, hidden_size, init_w=3e-3):
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super(Critic, self).__init__()
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self.linear1 = nn.Linear(n_obs + action_dim, hidden_size)
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self.linear1 = nn.Linear(n_obs + n_actions, hidden_size)
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self.linear2 = nn.Linear(hidden_size, hidden_size)
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self.linear3 = nn.Linear(hidden_size, 1)
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# 随机初始化为较小的值
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@@ -51,11 +51,11 @@ class Critic(nn.Module):
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return x
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class Actor(nn.Module):
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def __init__(self, n_obs, action_dim, hidden_size, init_w=3e-3):
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def __init__(self, n_obs, n_actions, hidden_size, init_w=3e-3):
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super(Actor, self).__init__()
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self.linear1 = nn.Linear(n_obs, hidden_size)
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self.linear2 = nn.Linear(hidden_size, hidden_size)
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self.linear3 = nn.Linear(hidden_size, action_dim)
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self.linear3 = nn.Linear(hidden_size, n_actions)
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self.linear3.weight.data.uniform_(-init_w, init_w)
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self.linear3.bias.data.uniform_(-init_w, init_w)
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@@ -67,18 +67,18 @@ class Actor(nn.Module):
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return x
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class ActorCritic(nn.Module):
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def __init__(self, state_dim, action_dim, hidden_dim=256):
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def __init__(self, n_states, n_actions, hidden_dim=256):
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super(ActorCritic, self).__init__()
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self.critic = nn.Sequential(
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nn.Linear(state_dim, hidden_dim),
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nn.Linear(n_states, hidden_dim),
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nn.ReLU(),
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nn.Linear(hidden_dim, 1)
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)
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self.actor = nn.Sequential(
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nn.Linear(state_dim, hidden_dim),
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nn.Linear(n_states, hidden_dim),
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nn.ReLU(),
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nn.Linear(hidden_dim, action_dim),
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nn.Linear(hidden_dim, n_actions),
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nn.Softmax(dim=1),
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)
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