146 lines
5.1 KiB
Python
146 lines
5.1 KiB
Python
"""DDPG agent using Stable-Baselines3 for VSL control."""
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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.nn as nn
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from gymnasium import spaces
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from stable_baselines3 import DDPG
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from stable_baselines3.common.noise import NormalActionNoise
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from stable_baselines3.common.torch_layers import FlattenExtractor
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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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def __init__(self, state_dim, action_dims):
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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(low=-np.inf, high=np.inf, shape=(state_dim,), dtype=np.float32)
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self.action_space = spaces.Box(low=0.0, high=1.0, shape=(self.num_zones,), dtype=np.float32)
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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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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 DDPG 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 DDPGAgent:
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"""DDPG 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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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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activation_fn: str = "relu",
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):
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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=float(exploration_sigma) * 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, [256, 256]),
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"qf": _as_arch_list(critic_hidden_dims, [256, 256]),
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},
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"activation_fn": _resolve_activation_fn(activation_fn),
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"features_extractor_class": FlattenExtractor,
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}
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self.model = DDPG(
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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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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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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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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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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)
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for i in range(self.num_zones)
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], dtype=np.float32)
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self.model.replay_buffer.add(state, next_state, continuous_action, reward, done, [{}])
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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 = DDPG.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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