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update codes

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johnjim0816
2021-12-21 20:14:13 +08:00
parent 64c319cab4
commit 3b712e8815
71 changed files with 1097 additions and 1340 deletions
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26
codes/common/model.py
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@@ -15,15 +15,15 @@ import torch.nn.functional as F
from torch.distributions import Categorical
class MLP(nn.Module):
def __init__(self, n_states,n_actions,hidden_dim=128):
def __init__(self, input_dim,output_dim,hidden_dim=128):
""" 初始化q网络，为全连接网络
n_states: 输入的特征数即环境的状态数
n_actions: 输出的动作维度
input_dim: 输入的特征数即环境的状态数
output_dim: 输出的动作维度
"""
super(MLP, self).__init__()
self.fc1 = nn.Linear(n_states, hidden_dim) # 输入层
self.fc1 = nn.Linear(input_dim, hidden_dim) # 输入层
self.fc2 = nn.Linear(hidden_dim,hidden_dim) # 隐藏层
self.fc3 = nn.Linear(hidden_dim, n_actions) # 输出层
self.fc3 = nn.Linear(hidden_dim, output_dim) # 输出层
def forward(self, x):
# 各层对应的激活函数
@@ -32,10 +32,10 @@ class MLP(nn.Module):
return self.fc3(x)
class Critic(nn.Module):
def __init__(self, n_obs, n_actions, hidden_size, init_w=3e-3):
def __init__(self, n_obs, action_dim, hidden_size, init_w=3e-3):
super(Critic, self).__init__()
self.linear1 = nn.Linear(n_obs + n_actions, hidden_size)
self.linear1 = nn.Linear(n_obs + action_dim, hidden_size)
self.linear2 = nn.Linear(hidden_size, hidden_size)
self.linear3 = nn.Linear(hidden_size, 1)
# 随机初始化为较小的值
@@ -51,11 +51,11 @@ class Critic(nn.Module):
return x
class Actor(nn.Module):
def __init__(self, n_obs, n_actions, hidden_size, init_w=3e-3):
def __init__(self, n_obs, action_dim, hidden_size, init_w=3e-3):
super(Actor, self).__init__()
self.linear1 = nn.Linear(n_obs, hidden_size)
self.linear2 = nn.Linear(hidden_size, hidden_size)
self.linear3 = nn.Linear(hidden_size, n_actions)
self.linear3 = nn.Linear(hidden_size, action_dim)
self.linear3.weight.data.uniform_(-init_w, init_w)
self.linear3.bias.data.uniform_(-init_w, init_w)
@@ -67,18 +67,18 @@ class Actor(nn.Module):
return x
class ActorCritic(nn.Module):
def __init__(self, n_states, n_actions, hidden_dim=256):
def __init__(self, state_dim, action_dim, hidden_dim=256):
super(ActorCritic, self).__init__()
self.critic = nn.Sequential(
nn.Linear(n_states, hidden_dim),
nn.Linear(state_dim, hidden_dim),
nn.ReLU(),
nn.Linear(hidden_dim, 1)
)
self.actor = nn.Sequential(
nn.Linear(n_states, hidden_dim),
nn.Linear(state_dim, hidden_dim),
nn.ReLU(),
nn.Linear(hidden_dim, n_actions),
nn.Linear(hidden_dim, action_dim),
nn.Softmax(dim=1),
)