364 lines
12 KiB
Python
364 lines
12 KiB
Python
"""
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APPO v2 - 改进的空间感知注意力机制
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将状态分解为zone级别表示，注意力在zone之间交互
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"""
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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import torch.optim as optim
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import numpy as np
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from typing import Tuple, Dict
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class SpatialAttention(nn.Module):
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"""空间感知的多头注意力"""
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def __init__(self, d_model: int, num_heads: int = 4):
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super().__init__()
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assert d_model % num_heads == 0
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self.d_model = d_model
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self.num_heads = num_heads
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self.d_k = d_model // num_heads
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self.W_q = nn.Linear(d_model, d_model)
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self.W_k = nn.Linear(d_model, d_model)
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self.W_v = nn.Linear(d_model, d_model)
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self.W_o = nn.Linear(d_model, d_model)
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self.dropout = nn.Dropout(0.1)
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self.layer_norm = nn.LayerNorm(d_model)
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def forward(self, x):
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batch_size, seq_len, _ = x.size()
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Q = self.W_q(x).view(batch_size, seq_len, self.num_heads, self.d_k).transpose(1, 2)
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K = self.W_k(x).view(batch_size, seq_len, self.num_heads, self.d_k).transpose(1, 2)
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V = self.W_v(x).view(batch_size, seq_len, self.num_heads, self.d_k).transpose(1, 2)
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scores = torch.matmul(Q, K.transpose(-2, -1)) / np.sqrt(self.d_k)
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attn = F.softmax(scores, dim=-1)
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attn = self.dropout(attn)
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context = torch.matmul(attn, V)
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context = context.transpose(1, 2).contiguous().view(batch_size, seq_len, self.d_model)
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output = self.W_o(context)
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return self.layer_norm(x + self.dropout(output))
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class SpatialActorCritic(nn.Module):
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"""空间感知的Actor-Critic网络"""
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def __init__(
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self,
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state_dim: int,
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num_actions: int,
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num_zones: int = 3,
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hidden_dim: int = 128,
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num_heads: int = 4,
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num_layers: int = 2,
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):
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super().__init__()
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self.state_dim = state_dim
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self.num_actions = num_actions
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self.num_zones = num_zones
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self.zone_dim = hidden_dim
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# 状态编码器：将全局状态投影到zone表示
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self.state_encoder = nn.Sequential(
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nn.Linear(state_dim, hidden_dim * num_zones),
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nn.ReLU(),
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nn.LayerNorm(hidden_dim * num_zones)
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)
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# 位置编码
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# self.pos_encoding = nn.Parameter(torch.randn(1, num_zones, hidden_dim))
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self.pos_encoding = nn.Parameter(torch.randn(1, num_zones, hidden_dim) * 0.02)
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# 空间注意力层
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self.attention_layers = nn.ModuleList([
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SpatialAttention(hidden_dim, num_heads) for _ in range(num_layers)
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])
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self.ffn_layers = nn.ModuleList([
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nn.Sequential(
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nn.Linear(hidden_dim, hidden_dim * 2),
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nn.ReLU(),
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nn.Dropout(0.1),
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nn.Linear(hidden_dim * 2, hidden_dim),
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nn.LayerNorm(hidden_dim)
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) for _ in range(num_layers)
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])
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# Actor: 全局池化后输出动作
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self.actor = nn.Sequential(
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nn.Linear(hidden_dim * num_zones, hidden_dim),
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nn.ReLU(),
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nn.Linear(hidden_dim, num_actions)
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)
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# Critic: 全局池化后输出价值
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self.critic = nn.Sequential(
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nn.Linear(hidden_dim * num_zones, 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._init_weights()
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def _init_weights(self):
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for m in self.modules():
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if isinstance(m, nn.Linear):
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nn.init.orthogonal_(m.weight, gain=np.sqrt(2))
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if m.bias is not None:
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nn.init.constant_(m.bias, 0)
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nn.init.orthogonal_(self.actor[-1].weight, gain=0.01)
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def forward(self, state):
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batch_size = state.size(0)
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x = self.state_encoder(state)
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x = x.view(batch_size, self.num_zones, self.zone_dim)
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x = x + self.pos_encoding
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for attn, ffn in zip(self.attention_layers, self.ffn_layers):
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x = attn(x)
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x = x + ffn(x)
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# 修复：将 x.view(batch_size, -1) 替换为平均池化，保留排列不变性
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global_feat = x.mean(dim=1)
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logits = self.actor(global_feat)
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value = self.critic(global_feat)
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return logits, value
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# def forward(self, state):
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# batch_size = state.size(0)
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# # 编码为zone表示
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# x = self.state_encoder(state)
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# x = x.view(batch_size, self.num_zones, self.zone_dim)
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# # 添加位置编码
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# x = x + self.pos_encoding
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# # 空间注意力
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# for attn, ffn in zip(self.attention_layers, self.ffn_layers):
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# x = attn(x)
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# x = x + ffn(x)
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# # 全局池化
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# global_feat = x.view(batch_size, -1)
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# logits = self.actor(global_feat)
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# value = self.critic(global_feat)
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# return logits, value
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def get_value(self, state):
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_, value = self.forward(state)
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return value
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class APPOv2Agent:
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"""改进的APPO Agent"""
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def __init__(
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self,
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state_dim: int,
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num_actions: int,
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num_zones: int = 3,
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hidden_dim: int = 128,
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num_heads: int = 4,
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num_layers: int = 2,
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learning_rate: float = 3e-4,
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gamma: float = 0.99,
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gae_lambda: float = 0.95,
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clip_epsilon: float = 0.2,
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value_coef: float = 0.5,
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entropy_coef: float = 0.02,
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max_grad_norm: float = 0.5,
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ppo_epochs: int = 10,
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minibatch_size: int = 64,
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device: str = "cuda",
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lr_schedule: str = "cosine",
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total_episodes: int = 300,
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):
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self.device = torch.device(device if torch.cuda.is_available() else "cpu")
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self.gamma = gamma
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self.gae_lambda = gae_lambda
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self.clip_epsilon = clip_epsilon
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self.value_coef = value_coef
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self.entropy_coef = entropy_coef
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self.max_grad_norm = max_grad_norm
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self.ppo_epochs = ppo_epochs
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self.minibatch_size = minibatch_size
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self.policy = SpatialActorCritic(
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state_dim, num_actions, num_zones, hidden_dim, num_heads, num_layers
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).to(self.device)
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self.optimizer = optim.Adam(self.policy.parameters(), lr=learning_rate, eps=1e-5)
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if lr_schedule == "cosine":
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self.scheduler = optim.lr_scheduler.CosineAnnealingLR(
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self.optimizer, T_max=total_episodes, eta_min=learning_rate * 0.1
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)
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else:
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self.scheduler = None
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self.reset_buffers()
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def reset_buffers(self):
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self.states = []
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self.actions = []
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self.rewards = []
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self.values = []
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self.log_probs = []
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self.dones = []
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def select_action(self, state: np.ndarray, deterministic: bool = False) -> Tuple[int, float, float]:
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state_tensor = torch.FloatTensor(state).unsqueeze(0).to(self.device)
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with torch.no_grad():
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logits, value = self.policy(state_tensor)
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probs = F.softmax(logits, dim=-1)
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if deterministic:
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action = torch.argmax(probs, dim=-1).item()
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else:
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dist = torch.distributions.Categorical(probs)
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action = dist.sample().item()
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log_prob = torch.log(probs[0, action] + 1e-10).item()
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return action, log_prob, value.item()
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def store_transition(self, state, action, reward, value, log_prob, done):
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self.states.append(state)
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self.actions.append(action)
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self.rewards.append(reward)
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self.values.append(value)
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self.log_probs.append(log_prob)
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self.dones.append(done)
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def compute_gae(self, next_value: float) -> Tuple[np.ndarray, np.ndarray]:
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# 预分配数组，将 O(N^2) 降维到 O(N)
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length = len(self.rewards)
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advantages = np.zeros(length, dtype=np.float32)
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gae = 0
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for t in reversed(range(length)):
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if t == length - 1:
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next_val = next_value
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else:
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next_val = self.values[t + 1]
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delta = self.rewards[t] + self.gamma * next_val * (1 - self.dones[t]) - self.values[t]
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gae = delta + self.gamma * self.gae_lambda * (1 - self.dones[t]) * gae
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advantages[t] = gae # 直接索引赋值，不要头插
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returns = advantages + np.array(self.values, dtype=np.float32)
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return advantages, returns
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# def compute_gae(self, next_value: float) -> Tuple[np.ndarray, np.ndarray]:
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# advantages = []
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# gae = 0
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# for t in reversed(range(len(self.rewards))):
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# if t == len(self.rewards) - 1:
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# next_val = next_value
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# else:
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# next_val = self.values[t + 1]
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# delta = self.rewards[t] + self.gamma * next_val * (1 - self.dones[t]) - self.values[t]
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# gae = delta + self.gamma * self.gae_lambda * (1 - self.dones[t]) * gae
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# advantages.insert(0, gae)
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# advantages = np.array(advantages, dtype=np.float32)
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# returns = advantages + np.array(self.values, dtype=np.float32)
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# return advantages, returns
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def update(self, next_value: float) -> Dict[str, float]:
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if len(self.states) == 0:
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return {}
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advantages, returns = self.compute_gae(next_value)
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advantages = (advantages - advantages.mean()) / (advantages.std() + 1e-8)
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states = torch.FloatTensor(np.array(self.states)).to(self.device)
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actions = torch.LongTensor(self.actions).to(self.device)
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old_log_probs = torch.FloatTensor(self.log_probs).to(self.device)
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advantages_t = torch.FloatTensor(advantages).to(self.device)
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returns_t = torch.FloatTensor(returns).to(self.device)
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total_loss = 0
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total_policy_loss = 0
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total_value_loss = 0
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total_entropy = 0
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update_count = 0
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dataset_size = len(self.states)
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for _ in range(self.ppo_epochs):
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indices = np.random.permutation(dataset_size)
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for start_idx in range(0, dataset_size, self.minibatch_size):
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end_idx = min(start_idx + self.minibatch_size, dataset_size)
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batch_idx = indices[start_idx:end_idx]
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batch_states = states[batch_idx]
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batch_actions = actions[batch_idx]
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batch_old_lp = old_log_probs[batch_idx]
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batch_adv = advantages_t[batch_idx]
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batch_ret = returns_t[batch_idx]
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logits, values = self.policy(batch_states)
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probs = F.softmax(logits, dim=-1)
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dist = torch.distributions.Categorical(probs)
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new_log_probs = dist.log_prob(batch_actions)
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entropy = dist.entropy().mean()
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ratio = torch.exp(new_log_probs - batch_old_lp)
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surr1 = ratio * batch_adv
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surr2 = torch.clamp(ratio, 1 - self.clip_epsilon, 1 + self.clip_epsilon) * batch_adv
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policy_loss = -torch.min(surr1, surr2).mean()
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value_loss = F.mse_loss(values.squeeze(), batch_ret)
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loss = policy_loss + self.value_coef * value_loss - self.entropy_coef * entropy
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self.optimizer.zero_grad()
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loss.backward()
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nn.utils.clip_grad_norm_(self.policy.parameters(), self.max_grad_norm)
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self.optimizer.step()
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total_loss += loss.item()
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total_policy_loss += policy_loss.item()
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total_value_loss += value_loss.item()
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total_entropy += entropy.item()
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update_count += 1
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if self.scheduler is not None:
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self.scheduler.step()
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self.reset_buffers()
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return {
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'loss': total_loss / max(update_count, 1),
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'policy_loss': total_policy_loss / max(update_count, 1),
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'value_loss': total_value_loss / max(update_count, 1),
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'entropy': total_entropy / max(update_count, 1),
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'lr': self.optimizer.param_groups[0]['lr'],
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}
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def save(self, path: str):
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torch.save({
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'policy_state_dict': self.policy.state_dict(),
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'optimizer_state_dict': self.optimizer.state_dict(),
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}, path)
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def load(self, path: str):
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checkpoint = torch.load(path, map_location=self.device, weights_only=False)
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self.policy.load_state_dict(checkpoint['policy_state_dict'])
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self.optimizer.load_state_dict(checkpoint['optimizer_state_dict'])
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