332 lines
12 KiB
Python
332 lines
12 KiB
Python
"""
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APPO agent for SUMO VSL with edge-structured tokenization.
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"""
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import numpy as np
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import torch
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import torch.nn as nn
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import torch.optim as optim
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from typing import Dict, List, Tuple
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class SpatialAttentionBlock(nn.Module):
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"""Self-attention block over ordered edge tokens."""
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def __init__(self, hidden_dim: int, num_heads: int = 4, dropout: float = 0.1):
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super().__init__()
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self.attn = nn.MultiheadAttention(
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embed_dim=hidden_dim,
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num_heads=num_heads,
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dropout=dropout,
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batch_first=True,
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)
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self.norm1 = nn.LayerNorm(hidden_dim)
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self.norm2 = nn.LayerNorm(hidden_dim)
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self.ffn = nn.Sequential(
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nn.Linear(hidden_dim, hidden_dim * 2),
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nn.GELU(),
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nn.Dropout(dropout),
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nn.Linear(hidden_dim * 2, hidden_dim),
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)
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self.dropout = nn.Dropout(dropout)
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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attn_out, _ = self.attn(x, x, x, need_weights=False)
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x = self.norm1(x + self.dropout(attn_out))
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ffn_out = self.ffn(x)
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return self.norm2(x + self.dropout(ffn_out))
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class MultiDiscreteActorCritic(nn.Module):
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"""Actor-critic that builds one token per controlled edge."""
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def __init__(
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self,
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state_dim: int,
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action_dims: List[int],
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edge_feature_dim: int = 3,
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time_feature_dim: 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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dropout: float = 0.1,
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):
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super().__init__()
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self.state_dim = state_dim
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self.action_dims = action_dims
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self.num_zones = len(action_dims)
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self.edge_feature_dim = edge_feature_dim
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self.speed_feature_dim = 1
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self.time_feature_dim = time_feature_dim
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self.last_reward_dim = 1
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self.global_feature_dim = self.time_feature_dim + self.last_reward_dim
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self.agent_id_dim = 1
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self.local_obs_dim = (
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self.edge_feature_dim
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+ self.speed_feature_dim
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+ self.global_feature_dim
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+ self.agent_id_dim
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)
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self.local_encoder = nn.Sequential(
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nn.Linear(self.local_obs_dim, hidden_dim),
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nn.LayerNorm(hidden_dim),
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nn.GELU(),
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)
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self.pos_encoding = nn.Parameter(torch.zeros(1, self.num_zones, hidden_dim))
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self.attention_layers = nn.ModuleList(
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[SpatialAttentionBlock(hidden_dim, num_heads=num_heads, dropout=dropout) for _ in range(num_layers)]
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)
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self.actor_heads = nn.ModuleList([nn.Linear(hidden_dim, adim) for adim in action_dims])
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self.critic = nn.Sequential(
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nn.Linear(hidden_dim * 2 + self.global_feature_dim, hidden_dim),
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nn.LayerNorm(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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agent_ids = torch.linspace(0.0, 1.0, self.num_zones, dtype=torch.float32)
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self.register_buffer("agent_id_features", agent_ids.view(1, self.num_zones, 1))
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self._init_weights()
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def _init_weights(self):
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for module in self.modules():
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if isinstance(module, nn.Linear):
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nn.init.orthogonal_(module.weight, gain=np.sqrt(2))
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if module.bias is not None:
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nn.init.constant_(module.bias, 0)
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for head in self.actor_heads:
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nn.init.orthogonal_(head.weight, gain=0.01)
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nn.init.orthogonal_(self.critic[-1].weight, gain=1.0)
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nn.init.normal_(self.pos_encoding, mean=0.0, std=0.02)
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def _build_local_tokens(self, state: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
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if state.dim() == 1:
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state = state.unsqueeze(0)
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batch_size = state.size(0)
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edge_block = self.num_zones * self.edge_feature_dim
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speed_block_start = edge_block
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speed_block_end = speed_block_start + self.num_zones
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global_block_start = speed_block_end
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global_block_end = global_block_start + self.global_feature_dim
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edge_features = state[:, :edge_block].view(batch_size, self.num_zones, self.edge_feature_dim)
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local_speed_limits = state[:, speed_block_start:speed_block_end].view(batch_size, self.num_zones, 1)
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global_features = state[:, global_block_start:global_block_end]
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repeated_global = global_features.unsqueeze(1).expand(-1, self.num_zones, -1)
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agent_ids = self.agent_id_features.expand(batch_size, -1, -1)
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tokens = torch.cat([edge_features, local_speed_limits, repeated_global, agent_ids], dim=-1)
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return tokens, global_features
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def forward(self, state: torch.Tensor) -> Tuple[List[torch.Tensor], torch.Tensor]:
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tokens, global_features = self._build_local_tokens(state)
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x = self.local_encoder(tokens) + self.pos_encoding
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for attention_layer in self.attention_layers:
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x = attention_layer(x)
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logits_list = [head(x[:, idx, :]) for idx, head in enumerate(self.actor_heads)]
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pooled = torch.cat([x.mean(dim=1), x.max(dim=1).values, global_features], dim=-1)
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value = self.critic(pooled)
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return logits_list, value
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def get_value(self, state: torch.Tensor) -> torch.Tensor:
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_, value = self.forward(state)
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return value
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class APPOAgent:
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"""APPO agent for SUMO MultiDiscrete action space."""
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def __init__(
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self,
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state_dim: int,
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action_dims: List[int],
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edge_feature_dim: int = 3,
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time_feature_dim: 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.action_dims = action_dims
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self.policy = MultiDiscreteActorCritic(
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state_dim=state_dim,
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action_dims=action_dims,
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edge_feature_dim=edge_feature_dim,
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time_feature_dim=time_feature_dim,
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hidden_dim=hidden_dim,
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num_heads=num_heads,
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num_layers=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,
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T_max=total_episodes,
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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[np.ndarray, 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_list, value = self.policy(state_tensor)
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actions = []
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log_prob_total = 0.0
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for logits in logits_list:
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dist = torch.distributions.Categorical(logits=logits)
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if deterministic:
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action = torch.argmax(logits, dim=-1).item()
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else:
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action = dist.sample().item()
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actions.append(action)
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log_prob_total += dist.log_prob(torch.tensor(action, device=self.device)).item()
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return np.array(actions, dtype=np.int64), log_prob_total, 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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advantages = []
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gae = 0.0
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for t in reversed(range(len(self.rewards))):
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next_val = next_value if t == len(self.rewards) - 1 else 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(np.array(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_policy_loss = 0.0
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total_value_loss = 0.0
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total_entropy = 0.0
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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_list, values = self.policy(batch_states)
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new_log_probs = torch.zeros(len(batch_idx), device=self.device)
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entropy = torch.zeros(len(batch_idx), device=self.device)
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for i, logits in enumerate(logits_list):
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dist = torch.distributions.Categorical(logits=logits)
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new_log_probs += dist.log_prob(batch_actions[:, i])
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entropy += dist.entropy()
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entropy_mean = 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 = nn.functional.mse_loss(values.squeeze(-1), batch_ret)
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loss = policy_loss + self.value_coef * value_loss - self.entropy_coef * entropy_mean
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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_policy_loss += policy_loss.item()
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total_value_loss += value_loss.item()
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total_entropy += entropy_mean.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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"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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}
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def save(self, path: str):
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torch.save(
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{
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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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},
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path,
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)
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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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