342 lines
12 KiB
Python
342 lines
12 KiB
Python
"""
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Structured Corridor TD3 (SC-TD3) for motorway VSL control.
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"""
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from __future__ import annotations
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from typing import List, Sequence
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import gymnasium as gym
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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.nn.functional as F
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from gymnasium import spaces
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from stable_baselines3 import TD3
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from stable_baselines3.common.noise import NormalActionNoise
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from stable_baselines3.common.torch_layers import BaseFeaturesExtractor
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from utils.sb3_manual import ensure_manual_logger, sync_manual_timesteps
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class MultiDiscreteWrapper(gym.Env):
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"""Wrap a MultiDiscrete action space as a continuous Box for TD3."""
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def __init__(self, state_dim: int, action_dims: Sequence[int]):
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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.observation_space = spaces.Box(
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low=-np.inf, high=np.inf, shape=(state_dim,), dtype=np.float32
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)
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self.action_space = spaces.Box(
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low=0.0, high=1.0, shape=(self.num_zones,), dtype=np.float32
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)
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def reset(self, seed=None, options=None):
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return np.zeros(self.state_dim, dtype=np.float32), {}
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def step(self, action):
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return np.zeros(self.state_dim, dtype=np.float32), 0.0, False, False, {}
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class EdgeLocalMixer(nn.Module):
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"""Lightweight local mixer that preserves strong per-edge identity."""
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def __init__(self, hidden_dim: int, kernel_size: int = 3):
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super().__init__()
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padding = (kernel_size - 1) // 2
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self.norm = nn.LayerNorm(hidden_dim)
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self.depthwise = nn.Conv1d(
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hidden_dim,
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hidden_dim,
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kernel_size=kernel_size,
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padding=padding,
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groups=hidden_dim,
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)
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self.pointwise = nn.Conv1d(hidden_dim, hidden_dim, kernel_size=1)
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self.ffn = nn.Sequential(
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nn.LayerNorm(hidden_dim),
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nn.Linear(hidden_dim, hidden_dim * 2),
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nn.SiLU(),
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nn.Linear(hidden_dim * 2, hidden_dim),
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)
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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y = self.norm(x).transpose(1, 2)
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y = F.silu(self.depthwise(y))
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y = self.pointwise(y).transpose(1, 2)
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# Keep spatial mixing weak to avoid washing out local bottlenecks.
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x = x + 0.25 * y
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x = x + 0.25 * self.ffn(x)
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return x
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class EdgeStructuredExtractor(BaseFeaturesExtractor):
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"""
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Feature extractor tailored to motorway VSL.
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State layout is assumed to be:
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- per-edge traffic features: [speed_norm, occ_norm, flow_norm] * num_edges
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- per-edge current limit feature: [limit_norm] * num_edges
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- global features: [time_progress, sin_t, cos_t, last_reward]
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"""
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def __init__(
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self,
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observation_space: spaces.Box,
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num_edges: int,
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edge_feature_dim: int = 3,
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global_feature_dim: int = 4,
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edge_hidden_dim: int = 16,
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global_hidden_dim: int = 32,
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spatial_blocks: int = 1,
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kernel_size: int = 3,
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features_dim: int = 128,
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):
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super().__init__(observation_space, features_dim)
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self.num_edges = num_edges
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self.edge_feature_dim = edge_feature_dim
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self.edge_input_dim = edge_feature_dim + 1
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self.edge_hidden_dim = edge_hidden_dim
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self.global_feature_dim = global_feature_dim
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self.edge_encoder = nn.Sequential(
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nn.Linear(self.edge_input_dim, edge_hidden_dim),
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nn.LayerNorm(edge_hidden_dim),
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nn.SiLU(),
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)
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self.delta_encoder = nn.Sequential(
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nn.Linear(edge_feature_dim, edge_hidden_dim),
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nn.LayerNorm(edge_hidden_dim),
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nn.SiLU(),
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)
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self.local_mixers = nn.ModuleList(
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[
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EdgeLocalMixer(
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hidden_dim=edge_hidden_dim,
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kernel_size=kernel_size,
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)
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for idx in range(spatial_blocks)
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]
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)
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self.global_encoder = nn.Sequential(
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nn.Linear(global_feature_dim + self.edge_input_dim * 2, global_hidden_dim),
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nn.LayerNorm(global_hidden_dim),
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nn.SiLU(),
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)
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self.edge_gate = nn.Linear(global_hidden_dim, edge_hidden_dim)
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self.edge_bias = nn.Linear(global_hidden_dim, edge_hidden_dim)
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raw_input_dim = num_edges * self.edge_input_dim
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delta_input_dim = num_edges * edge_feature_dim
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fused_input_dim = num_edges * edge_hidden_dim + raw_input_dim + delta_input_dim + global_hidden_dim
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self.output_proj = nn.Sequential(
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nn.Linear(fused_input_dim, features_dim),
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nn.LayerNorm(features_dim),
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nn.SiLU(),
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)
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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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nn.init.constant_(module.bias, 0.0)
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elif isinstance(module, nn.Conv1d):
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nn.init.kaiming_normal_(module.weight, nonlinearity="relu")
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if module.bias is not None:
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nn.init.constant_(module.bias, 0.0)
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def forward(self, observations: torch.Tensor) -> torch.Tensor:
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batch_size = observations.shape[0]
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edge_traffic_dim = self.num_edges * self.edge_feature_dim
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edge_limit_dim = self.num_edges
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edge_traffic = observations[:, :edge_traffic_dim].view(
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batch_size, self.num_edges, self.edge_feature_dim
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)
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edge_limits = observations[
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:, edge_traffic_dim : edge_traffic_dim + edge_limit_dim
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].view(batch_size, self.num_edges, 1)
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global_features = observations[:, edge_traffic_dim + edge_limit_dim :]
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edge_inputs = torch.cat([edge_traffic, edge_limits], dim=-1)
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edge_latent = self.edge_encoder(edge_inputs)
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edge_deltas = torch.zeros_like(edge_traffic)
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edge_deltas[:, 1:, :] = edge_traffic[:, 1:, :] - edge_traffic[:, :-1, :]
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delta_latent = self.delta_encoder(edge_deltas)
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edge_latent = edge_latent + 0.5 * delta_latent
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for mixer in self.local_mixers:
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edge_latent = mixer(edge_latent)
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edge_inputs_mean = edge_inputs.mean(dim=1)
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edge_inputs_max = edge_inputs.max(dim=1).values
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global_latent = self.global_encoder(
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torch.cat([global_features, edge_inputs_mean, edge_inputs_max], dim=-1)
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)
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gate = torch.tanh(self.edge_gate(global_latent)).unsqueeze(1)
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bias = torch.tanh(self.edge_bias(global_latent)).unsqueeze(1)
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edge_latent = edge_latent * (1.0 + gate) + bias
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fused = torch.cat(
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[
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edge_latent.reshape(batch_size, -1),
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edge_inputs.reshape(batch_size, -1),
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edge_deltas.reshape(batch_size, -1),
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global_latent,
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],
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dim=-1,
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)
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return self.output_proj(fused)
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def _resolve_activation_fn(name: str):
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key = (name or "relu").strip().lower()
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if key == "relu":
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return nn.ReLU
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if key == "silu":
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return nn.SiLU
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if key == "elu":
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return nn.ELU
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raise ValueError(f"Unsupported SC-TD3 activation: {name}")
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def _as_arch_list(value, default: List[int]) -> List[int]:
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if value is None:
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return list(default)
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return [int(v) for v in value]
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class SCTD3Agent:
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"""Structured Corridor TD3 agent wrapper."""
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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,
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learning_rate: float = 3e-4,
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buffer_size: int = 100000,
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learning_starts: int = 100,
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batch_size: int = 64,
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tau: float = 0.005,
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gamma: float = 0.99,
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policy_delay: int = 2,
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exploration_sigma: float = 0.1,
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device: str = "cuda",
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actor_hidden_dims: Sequence[int] | None = None,
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critic_hidden_dims: Sequence[int] | None = None,
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edge_feature_dim: int = 3,
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extractor_feature_dim: int = 128,
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extractor_edge_hidden_dim: int = 16,
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extractor_global_hidden_dim: int = 32,
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extractor_spatial_blocks: int = 1,
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extractor_kernel_size: int = 3,
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activation_fn: str = "silu",
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):
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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.device = device
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self.learning_starts = learning_starts
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self.total_steps = 0
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self.exploration_sigma = exploration_sigma
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dummy_env = MultiDiscreteWrapper(state_dim, action_dims)
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action_noise = NormalActionNoise(
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mean=np.zeros(self.num_zones),
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sigma=0.1 * np.ones(self.num_zones),
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)
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policy_kwargs = {
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"net_arch": {
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"pi": _as_arch_list(actor_hidden_dims, [128, 64]),
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"qf": _as_arch_list(critic_hidden_dims, [192, 128]),
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},
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"activation_fn": _resolve_activation_fn(activation_fn),
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"features_extractor_class": EdgeStructuredExtractor,
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"features_extractor_kwargs": {
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"num_edges": self.num_zones,
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"edge_feature_dim": edge_feature_dim,
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"global_feature_dim": 4,
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"edge_hidden_dim": extractor_edge_hidden_dim,
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"global_hidden_dim": extractor_global_hidden_dim,
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"spatial_blocks": extractor_spatial_blocks,
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"kernel_size": extractor_kernel_size,
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"features_dim": extractor_feature_dim,
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},
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"share_features_extractor": False,
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}
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self.model = TD3(
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"MlpPolicy",
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env=dummy_env,
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learning_rate=learning_rate,
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buffer_size=buffer_size,
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learning_starts=learning_starts,
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batch_size=batch_size,
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tau=tau,
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gamma=gamma,
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policy_delay=policy_delay,
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action_noise=action_noise,
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device=device,
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verbose=0,
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policy_kwargs=policy_kwargs,
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)
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ensure_manual_logger(self.model)
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def select_action(self, state: np.ndarray, deterministic: bool = False):
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if not deterministic and self.total_steps < self.learning_starts:
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discrete_action = np.array(
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[np.random.randint(self.action_dims[i]) for i in range(self.num_zones)],
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dtype=np.int64,
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)
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return discrete_action, 0.0, 0.0
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continuous_action, _ = self.model.predict(state, deterministic=deterministic)
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if not deterministic:
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noise = np.random.normal(0.0, self.exploration_sigma, size=self.num_zones)
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continuous_action = np.clip(continuous_action + noise, 0.0, 1.0)
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discrete_action = np.array(
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[
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int(cont * (self.action_dims[i] - 1) + 0.5)
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for i, cont in enumerate(continuous_action)
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]
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)
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discrete_action = np.clip(discrete_action, 0, [d - 1 for d in self.action_dims])
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return discrete_action, 0.0, 0.0
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def store_transition(self, state, action, reward, next_state, done):
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self.total_steps += 1
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sync_manual_timesteps(self.model, self.total_steps)
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continuous_action = np.array(
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[action[i] / (self.action_dims[i] - 1) for i in range(self.num_zones)],
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dtype=np.float32,
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)
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self.model.replay_buffer.add(
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state, next_state, continuous_action, reward, done, [{}]
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)
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def update(self):
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if self.model.replay_buffer.size() < self.model.batch_size:
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return {}
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self.model.train(gradient_steps=1, batch_size=self.model.batch_size)
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return {"updates": float(self.model._n_updates)}
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def save(self, path: str):
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self.model.save(path)
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def load(self, path: str):
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self.model = TD3.load(path, device=self.device)
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ensure_manual_logger(self.model)
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self.total_steps = int(getattr(self.model, "num_timesteps", 0))
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