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Merge remote-tracking branch 'refs/remotes/origin/HL' into HL
2 parents b3b4951 + d3d878f commit 0a33030

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Lines changed: 136 additions & 173 deletions

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‎.gitignore‎

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.venv
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*.onnx
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checkpoints
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*.wbproj

‎README.md‎

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@@ -33,7 +33,7 @@ uv sync --extra rpi
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Navigate to the simulator directory.
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```bash
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cd src/Simulateur
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cd scripts
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```
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Run the multi-process training script.

‎scripts/launch_train_multiprocessing.py‎

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import os
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import sys
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from typing import *
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import torch.nn as nn
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from stable_baselines3 import PPO
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from stable_baselines3.common.vec_env import SubprocVecEnv
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simu_path = __file__.rsplit('/', 2)[0] + '/src/Simulateur'
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if simu_path not in sys.path:
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sys.path.insert(0, simu_path)
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from config import *
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from TemporalResNetExtractor import TemporalResNetExtractor
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from onnx_utils import *
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from WebotsSimulationGymEnvironment import WebotsSimulationGymEnvironment
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if B_DEBUG: from DynamicActionPlotCallback import DynamicActionPlotDistributionCallback
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def log(s: str):
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if B_DEBUG:
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print(s, file=open("/tmp/autotech/logs", "a"))
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if __name__ == "__main__":
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if not os.path.exists("/tmp/autotech/"):
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os.mkdir("/tmp/autotech/")
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os.system('if [ -n "$(ls /tmp/autotech)" ]; then rm /tmp/autotech/*; fi')
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if B_DEBUG:
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print("Webots started", file=open("/tmp/autotech/logs", "w"))
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def make_env(rank: int):
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log(f"CAREFUL !!! created an SERVER env with {rank=}")
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return WebotsSimulationGymEnvironment(rank)
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envs = SubprocVecEnv([lambda rank=rank : make_env(rank) for rank in range(n_simulations)])
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ExtractorClass = TemporalResNetExtractor
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policy_kwargs = dict(
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features_extractor_class=ExtractorClass,
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features_extractor_kwargs=dict(
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context_size=context_size,
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lidar_horizontal_resolution=lidar_horizontal_resolution,
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camera_horizontal_resolution=camera_horizontal_resolution,
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device=device
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),
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activation_fn=nn.ReLU,
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net_arch=[512, 512, 512],
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)
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ppo_args = dict(
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n_steps=4096,
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n_epochs=10,
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batch_size=256,
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learning_rate=3e-4,
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gamma=0.99,
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verbose=1,
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normalize_advantage=True,
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device=device
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)
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save_path = __file__.rsplit("/", 1)[0] + "/checkpoints/" + ExtractorClass.__name__ + "/"
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os.makedirs(save_path, exist_ok=True)
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print(save_path)
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print(os.listdir(save_path))
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valid_files = [x for x in os.listdir(save_path) if x.rstrip(".zip").isnumeric()]
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if valid_files:
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model_name = max(
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valid_files,
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key=lambda x : int(x.rstrip(".zip"))
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)
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print(f"Loading model {save_path + model_name}")
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model = PPO.load(
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save_path + model_name,
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envs,
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**ppo_args,
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policy_kwargs=policy_kwargs
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)
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i = int(model_name.rstrip(".zip")) + 1
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print(f"----- Model found, loading {model_name} -----")
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else:
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model = PPO(
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"MlpPolicy",
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envs,
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**ppo_args,
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policy_kwargs=policy_kwargs
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)
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i = 0
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print("----- Model not found, creating a new one -----")
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print("MODEL HAS HYPER PARAMETERS:")
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print(f"{model.learning_rate=}")
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print(f"{model.gamma=}")
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print(f"{model.verbose=}")
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print(f"{model.n_steps=}")
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print(f"{model.n_epochs=}")
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print(f"{model.batch_size=}")
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print(f"{model.device=}")
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log(f"SERVER : finished executing")
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# obs = envs.reset()
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# while True:
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# action, _states = model.predict(obs, deterministic=True) # Use deterministic=True for evaluation
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# obs, reward, done, info = envs.step(action)
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# envs.render() # Optional: visualize the environment
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while True:
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export_onnx(model)
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test_onnx(model)
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if B_DEBUG:
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model.learn(total_timesteps=500_000, callback=DynamicActionPlotDistributionCallback())
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else:
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model.learn(total_timesteps=500_000)
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model.save(save_path + str(i))
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i += 1

src/Simulateur/launch_train_multiprocessing.py renamed to src/Simulateur/WebotsSimulationGymEnvironment.py

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@@ -1,27 +1,9 @@
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import os
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import time
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from typing import *
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import matplotlib.pyplot as plt
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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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import torch.multiprocessing as mp
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from stable_baselines3 import PPO
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from stable_baselines3.common.env_checker import check_env
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from stable_baselines3.common.vec_env import SubprocVecEnv, DummyVecEnv
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import gymnasium as gym
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from onnx_utils import export_onnx, test_onnx
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from config import *
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from CNN1DExtractor import CNN1DExtractor
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from TemporalResNetExtractor import TemporalResNetExtractor
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from CNN1DResNetExtractor import CNN1DResNetExtractor
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if B_DEBUG: from DynamicActionPlotCallback import DynamicActionPlotDistributionCallback
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def log(s: str):
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# if self.simulation_rank == 0:
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# print(f"{(obs[0] == 0).mean():.3f} {(obs[1] == 0).mean():.3f}")
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return obs, reward, done, truncated, info
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if __name__ == "__main__":
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if not os.path.exists("/tmp/autotech/"):
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os.mkdir("/tmp/autotech/")
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os.system('if [ -n "$(ls /tmp/autotech)" ]; then rm /tmp/autotech/*; fi')
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if B_DEBUG:
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print("Webots started", file=open("/tmp/autotech/logs", "w"))
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def make_env(rank: int, rank_v: int):
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log(f"CAREFUL !!! created an SERVER env with {rank=}")
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return WebotsSimulationGymEnvironment(rank, rank_v)
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envs = SubprocVecEnv([lambda rank=rank, rank_v =rank_v : make_env(rank, rank_v) for rank_v in range(n_vehicles) for rank in range(n_simulations)])
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ExtractorClass = TemporalResNetExtractor
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policy_kwargs = dict(
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features_extractor_class=ExtractorClass,
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features_extractor_kwargs=dict(
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context_size=context_size,
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lidar_horizontal_resolution=lidar_horizontal_resolution,
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camera_horizontal_resolution=camera_horizontal_resolution,
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device=device
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),
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activation_fn=nn.ReLU,
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net_arch=[512, 512, 512],
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)
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ppo_args = dict(
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n_steps=4096,
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n_epochs=10,
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batch_size=256,
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learning_rate=3e-4,
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gamma=0.99,
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verbose=1,
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normalize_advantage=True,
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device=device
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)
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save_path = __file__.rsplit("/", 1)[0] + "/checkpoints/" + ExtractorClass.__name__ + "/"
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if not os.path.exists(save_path):
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os.mkdir(save_path)
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print(save_path)
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print(os.listdir(save_path))
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valid_files = [x for x in os.listdir(save_path) if x.rstrip(".zip").isnumeric()]
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if valid_files:
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model_name = max(
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valid_files,
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key=lambda x : int(x.rstrip(".zip"))
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)
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print(f"Loading model {save_path + model_name}")
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model = PPO.load(
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save_path + model_name,
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envs,
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**ppo_args,
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policy_kwargs=policy_kwargs
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)
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i = int(model_name.rstrip(".zip")) + 1
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print(f"----- Model found, loading {model_name} -----")
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else:
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model = PPO(
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"MlpPolicy",
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envs,
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**ppo_args,
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policy_kwargs=policy_kwargs
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)
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i = 0
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print("----- Model not found, creating a new one -----")
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print("MODEL HAS HYPER PARAMETERS:")
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print(f"{model.learning_rate=}")
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print(f"{model.gamma=}")
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print(f"{model.verbose=}")
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print(f"{model.n_steps=}")
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print(f"{model.n_epochs=}")
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print(f"{model.batch_size=}")
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print(f"{model.device=}")
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log(f"SERVER : finished executing")
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# obs = envs.reset()
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# while True:
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# action, _states = model.predict(obs, deterministic=True) # Use deterministic=True for evaluation
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# obs, reward, done, info = envs.step(action)
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# envs.render() # Optional: visualize the environment
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while True:
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export_onnx(model)
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test_onnx(model)
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if B_DEBUG:
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model.learn(total_timesteps=500_000, callback=DynamicActionPlotDistributionCallback())
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else:
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model.learn(total_timesteps=500_000)
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model.save(save_path + str(i))
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i += 1

‎src/Simulateur/__init__.py‎

Whitespace-only changes.

‎src/Simulateur/config.py‎

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from torch.cuda import is_available
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n_map = 2
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n_simulations = 8
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n_simulations = 2
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n_vehicles = 1
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n_stupid_vehicles = 0
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n_actions_steering = 16

‎src/Simulateur/worlds/.piste.wbproj‎

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‎src/Simulateur/worlds/.piste0.wbproj‎

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‎src/Simulateur/worlds/.piste1.wbproj‎

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‎src/Simulateur/worlds/.piste2.wbproj‎

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