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JohnJim0816
2021-03-28 11:18:52 +08:00
parent 2df8d965d2
commit 6e4d966e1f
56 changed files with 497 additions and 165 deletions
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32
codes/common/model.py
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@@ -5,7 +5,7 @@ Author: John
Email: johnjim0816@gmail.com
Date: 2021-03-12 21:14:12
LastEditor: John
LastEditTime: 2021-03-23 16:35:46
LastEditTime: 2021-03-24 22:15:00
Discription:
Environment:
'''
@@ -14,16 +14,16 @@ import torch.nn as nn
import torch.nn.functional as F
from torch.distributions import Categorical
class MLP2(nn.Module):
def __init__(self, n_states,n_actions,hidden_dim=128):
class MLP(nn.Module):
def __init__(self, state_dim,action_dim,hidden_dim=128):
""" 初始化q网络，为全连接网络
n_states: 输入的feature即环境的state数目
n_actions: 输出的action总个数
state_dim: 输入的feature即环境的state数目
action_dim: 输出的action总个数
"""
super(MLP2, self).__init__()
self.fc1 = nn.Linear(n_states, hidden_dim) # 输入层
super(MLP, self).__init__()
self.fc1 = nn.Linear(state_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, action_dim) # 输出层
def forward(self, x):
# 各层对应的激活函数
@@ -32,10 +32,10 @@ class MLP2(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),
)