添加 A2C/QAC 算法实现及训练结果

- 新增 RL_Algothrithms 模块，包含 A2C、QAC 智能体
- 添加 SAC 章节笔记和 C10 笔记
- 上传训练结果图片
- 完善 README 与 .gitignore

RL_Algothrithms/networks.py

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+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+# 策略网络 (离散动作 Actor)
+class Actor(nn.Module):
+    def __init__(self, state_dim, action_dim):
+        super(Actor, self).__init__()
+        # 定义两层隐藏层
+        self.fc1 = nn.Linear(state_dim, 128)
+        self.fc2 = nn.Linear(128, action_dim)
+        
+    def forward(self, state):
+        x = F.relu(self.fc1(state))
+        # 输出动作的概率分布
+        action_probs = F.softmax(self.fc2(x), dim=-1)
+        return action_probs
+
+# Q值网络 (用于 QAC)
+class QCritic(nn.Module):
+    def __init__(self, state_dim, action_dim):
+        super(QCritic, self).__init__()
+        self.fc1 = nn.Linear(state_dim, 128)
+        self.fc2 = nn.Linear(128, action_dim)
+        
+    def forward(self, state):
+        x = F.relu(self.fc1(state))
+        # 输出各个动作的具体价值
+        q_values = self.fc2(x)
+        return q_values
+
+# V值网络 (用于 A2C，由于加入了基线，只需输出状态价值标量)
+class VCritic(nn.Module):
+    def __init__(self, state_dim):
+        super(VCritic, self).__init__()
+        self.fc1 = nn.Linear(state_dim, 128)
+        self.fc2 = nn.Linear(128, 1)
+        
+    def forward(self, state):
+        x = F.relu(self.fc1(state))
+        # 输出当前状态的价值评估
+        v_value = self.fc2(x)
+        return v_value