125 lines
4.0 KiB
Python
125 lines
4.0 KiB
Python
"""
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TD3 Agent using Stable-Baselines3
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Adapted for MultiDiscrete VSL control.
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"""
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import numpy as np
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import torch
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from stable_baselines3 import TD3
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from stable_baselines3.common.noise import NormalActionNoise
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import gymnasium as gym
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from gymnasium import spaces
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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, 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(
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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 TD3Agent:
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"""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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):
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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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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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)
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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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[int(cont * (self.action_dims[i] - 1) + 0.5) 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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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.learn(total_timesteps=1, reset_num_timesteps=False, log_interval=None)
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return {"actor_loss": 0.0, "critic_loss": 0.0}
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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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