修改同时训练时保存的目录

run_all_training.py

@@ -1,24 +1,50 @@
-"""一键异步启动全部训练"""
+"""一键异步启动全部训练。"""
+import os
 import subprocess
 import sys
 from datetime import datetime
 
 AGENTS = ["ppo", "appo", "mappo", "dqn", "ddpg", "td3"]
 
-processes = {}
-for agent in AGENTS:
-    print(f"[{datetime.now().strftime('%H:%M:%S')}] 启动 {agent.upper()} 训练...")
-    p = subprocess.Popen(
-        [sys.executable, "-m", f"training.train_{agent}"],
-        stdout=open(f"logs_{agent}_stdout.txt", "w"),
-        stderr=subprocess.STDOUT,
-    )
-    processes[agent] = p
-    print(f"  PID: {p.pid}")
 
-print(f"\n全部 {len(AGENTS)} 个训练已启动，等待完成...\n")
+def main():
+    run_timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+    processes = {}
 
-for agent, p in processes.items():
+    for agent in AGENTS:
-    p.wait()
+        print(f"[{datetime.now().strftime('%H:%M:%S')}] 启动 {agent.upper()} 训练...")
-    status = "✓ 完成" if p.returncode == 0 else f"✗ 失败(code={p.returncode})"
+        log_dir = os.path.join("logs", "multi-model", run_timestamp, agent)
-    print(f"[{agent.upper()}] {status}")
+        checkpoint_dir = os.path.join("checkpoints", "multi-model", run_timestamp, agent)
+        os.makedirs(log_dir, exist_ok=True)
+        os.makedirs(checkpoint_dir, exist_ok=True)
+        stdout_path = os.path.join(log_dir, "stdout.txt")
+
+        process = subprocess.Popen(
+            [
+                sys.executable,
+                "-m",
+                f"training.train_{agent}",
+                "--log-dir",
+                log_dir,
+                "--checkpoint-dir",
+                checkpoint_dir,
+                "--run-timestamp",
+                run_timestamp,
+            ],
+            stdout=open(stdout_path, "w", encoding="utf-8"),
+            stderr=subprocess.STDOUT,
+        )
+        processes[agent] = process
+        print(f"  PID: {process.pid}")
+
+    print(f"\n全部 {len(AGENTS)} 个训练已启动，等待完成...")
+    print(f"本次多模型时间戳: {run_timestamp}\n")
+
+    for agent, process in processes.items():
+        process.wait()
+        status = "完成" if process.returncode == 0 else f"失败(code={process.returncode})"
+        print(f"[{agent.upper()}] {status}")
+
+
+if __name__ == "__main__":
+    main()

training/train_appo.py

@@ -2,6 +2,7 @@
 基于 SUMO+TraCI 的 APPO 训练脚本
 使用微观仿真环境训练 VSL 控制策略
 """
+import argparse
 import os
 import sys
 import copy
@@ -19,9 +20,10 @@ from agents.appo_agent import APPOAgent
 from utils.config import get_agent_config, get_training_config
 from utils.logger import TrainingLogger
 from utils.plot import plot_training_curves
+from utils.run_dirs import add_run_dir_args, resolve_run_dirs
 
 
-def train_sumo_appo():
+def train_sumo_appo(log_dir=None, checkpoint_dir=None, run_timestamp=None):
     """SUMO 环境下的 APPO 训练主函数"""
     with open("config_sumo_vsl.yaml", "r", encoding="utf-8") as f:
         config = yaml.safe_load(f)
@@ -30,9 +32,12 @@ def train_sumo_appo():
     train_config = get_training_config(config)
 
     start_episode = 1
-    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+    _, checkpoint_dir, log_dir = resolve_run_dirs(
-    checkpoint_dir = os.path.join("checkpoints", "appo", timestamp)
+        "appo",
-    log_dir = os.path.join("logs", "appo", timestamp)
+        log_dir=log_dir,
+        checkpoint_dir=checkpoint_dir,
+        run_timestamp=run_timestamp,
+    )
     os.makedirs(checkpoint_dir, exist_ok=True)
     os.makedirs(log_dir, exist_ok=True)
     runtime_config = copy.deepcopy(config)
@@ -246,4 +251,10 @@ def train_sumo_appo():
 
 
 if __name__ == "__main__":
-    train_sumo_appo()
+    parser = add_run_dir_args(argparse.ArgumentParser())
+    args = parser.parse_args()
+    train_sumo_appo(
+        log_dir=args.log_dir,
+        checkpoint_dir=args.checkpoint_dir,
+        run_timestamp=args.run_timestamp,
+    )

training/train_ddpg.py

@@ -2,6 +2,7 @@
 基于 SUMO+TraCI 的 DDPG 训练脚本
 使用 Stable-Baselines3 的 DDPG 算法
 """
+import argparse
 import os
 import copy
 import yaml
@@ -16,9 +17,10 @@ from agents.ddpg_agent import DDPGAgent
 from utils.config import get_agent_config, get_training_config
 from utils.logger import TrainingLogger
 from utils.plot import plot_training_curves
+from utils.run_dirs import add_run_dir_args, resolve_run_dirs
 
 
-def train_sumo_ddpg():
+def train_sumo_ddpg(log_dir=None, checkpoint_dir=None, run_timestamp=None):
     """SUMO 环境下的 DDPG 训练主函数"""
     with open("config_sumo_vsl.yaml", "r", encoding="utf-8") as f:
         config = yaml.safe_load(f)
@@ -26,9 +28,12 @@ def train_sumo_ddpg():
     agent_config = get_agent_config(config, "ddpg")
     train_config = get_training_config(config)
 
-    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+    _, checkpoint_dir, log_dir = resolve_run_dirs(
-    checkpoint_dir = os.path.join("checkpoints", "ddpg", timestamp)
+        "ddpg",
-    log_dir = os.path.join("logs", "ddpg", timestamp)
+        log_dir=log_dir,
+        checkpoint_dir=checkpoint_dir,
+        run_timestamp=run_timestamp,
+    )
     os.makedirs(checkpoint_dir, exist_ok=True)
     os.makedirs(log_dir, exist_ok=True)
     runtime_config = copy.deepcopy(config)
@@ -183,4 +188,10 @@ def train_sumo_ddpg():
 
 
 if __name__ == "__main__":
-    train_sumo_ddpg()
+    parser = add_run_dir_args(argparse.ArgumentParser())
+    args = parser.parse_args()
+    train_sumo_ddpg(
+        log_dir=args.log_dir,
+        checkpoint_dir=args.checkpoint_dir,
+        run_timestamp=args.run_timestamp,
+    )

training/train_dqn.py

@@ -1,6 +1,7 @@
 """
 DQN训练脚本 - SUMO VSL环境
 """
+import argparse
 import os
 import sys
 import copy
@@ -17,9 +18,10 @@ from agents.dqn_agent import DQNAgent
 from utils.config import get_agent_config, get_training_config
 from utils.logger import TrainingLogger
 from utils.plot import plot_training_curves
+from utils.run_dirs import add_run_dir_args, resolve_run_dirs
 
 
-def train_sumo_dqn():
+def train_sumo_dqn(log_dir=None, checkpoint_dir=None, run_timestamp=None):
     with open("config_sumo_vsl.yaml", "r", encoding="utf-8") as f:
         config = yaml.safe_load(f)
 
@@ -27,9 +29,12 @@ def train_sumo_dqn():
     train_config = get_training_config(config)
 
     start_episode = 1
-    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+    _, checkpoint_dir, log_dir = resolve_run_dirs(
-    checkpoint_dir = os.path.join("checkpoints", "dqn", timestamp)
+        "dqn",
-    log_dir = os.path.join("logs", "dqn", timestamp)
+        log_dir=log_dir,
+        checkpoint_dir=checkpoint_dir,
+        run_timestamp=run_timestamp,
+    )
     os.makedirs(checkpoint_dir, exist_ok=True)
     os.makedirs(log_dir, exist_ok=True)
     runtime_config = copy.deepcopy(config)
@@ -203,4 +208,10 @@ def train_sumo_dqn():
 
 
 if __name__ == "__main__":
-    train_sumo_dqn()
+    parser = add_run_dir_args(argparse.ArgumentParser())
+    args = parser.parse_args()
+    train_sumo_dqn(
+        log_dir=args.log_dir,
+        checkpoint_dir=args.checkpoint_dir,
+        run_timestamp=args.run_timestamp,
+    )

training/train_mappo.py

@@ -1,6 +1,7 @@
 """
 MAPPO training script for SUMO + TraCI VSL.
 """
+import argparse
 import os
 import copy
 import yaml
@@ -16,18 +17,22 @@ from agents.mappo_agent import MAPPOAgent
 from utils.config import get_agent_config, get_training_config
 from utils.logger import TrainingLogger
 from utils.plot import plot_training_curves
+from utils.run_dirs import add_run_dir_args, resolve_run_dirs
 
 
-def train_sumo_mappo():
+def train_sumo_mappo(log_dir=None, checkpoint_dir=None, run_timestamp=None):
     with open("config_sumo_vsl.yaml", "r", encoding="utf-8") as f:
         config = yaml.safe_load(f)
 
     agent_config = get_agent_config(config, "mappo")
     train_config = get_training_config(config)
 
-    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+    _, checkpoint_dir, log_dir = resolve_run_dirs(
-    checkpoint_dir = os.path.join("checkpoints", "mappo", timestamp)
+        "mappo",
-    log_dir = os.path.join("logs", "mappo", timestamp)
+        log_dir=log_dir,
+        checkpoint_dir=checkpoint_dir,
+        run_timestamp=run_timestamp,
+    )
     os.makedirs(checkpoint_dir, exist_ok=True)
     os.makedirs(log_dir, exist_ok=True)
     runtime_config = copy.deepcopy(config)
@@ -235,4 +240,10 @@ def train_sumo_mappo():
 
 
 if __name__ == "__main__":
-    train_sumo_mappo()
+    parser = add_run_dir_args(argparse.ArgumentParser())
+    args = parser.parse_args()
+    train_sumo_mappo(
+        log_dir=args.log_dir,
+        checkpoint_dir=args.checkpoint_dir,
+        run_timestamp=args.run_timestamp,
+    )

training/train_ppo.py

@@ -2,6 +2,7 @@
 基于 SUMO+TraCI 的 PPO 训练脚本
 使用微观仿真环境训练 VSL 控制策略
 """
+import argparse
 import os
 import sys
 import copy
@@ -19,9 +20,10 @@ from agents.ppo_agent import PPOAgent
 from utils.config import get_agent_config, get_training_config
 from utils.logger import TrainingLogger
 from utils.plot import plot_training_curves
+from utils.run_dirs import add_run_dir_args, resolve_run_dirs
 
 
-def train_sumo_ppo():
+def train_sumo_ppo(log_dir=None, checkpoint_dir=None, run_timestamp=None):
     """SUMO 环境下的 PPO 训练主函数"""
     # 加载配置
     with open("config_sumo_vsl.yaml", "r", encoding="utf-8") as f:
@@ -31,9 +33,12 @@ def train_sumo_ppo():
     train_config = get_training_config(config)
 
     start_episode = 1
-    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+    _, checkpoint_dir, log_dir = resolve_run_dirs(
-    checkpoint_dir = os.path.join("checkpoints", "ppo", timestamp)
+        "ppo",
-    log_dir = os.path.join("logs", "ppo", timestamp)
+        log_dir=log_dir,
+        checkpoint_dir=checkpoint_dir,
+        run_timestamp=run_timestamp,
+    )
     os.makedirs(checkpoint_dir, exist_ok=True)
     os.makedirs(log_dir, exist_ok=True)
     runtime_config = copy.deepcopy(config)
@@ -249,4 +254,10 @@ def train_sumo_ppo():
 
 
 if __name__ == "__main__":
-    train_sumo_ppo()
+    parser = add_run_dir_args(argparse.ArgumentParser())
+    args = parser.parse_args()
+    train_sumo_ppo(
+        log_dir=args.log_dir,
+        checkpoint_dir=args.checkpoint_dir,
+        run_timestamp=args.run_timestamp,
+    )

training/train_td3.py

@@ -2,6 +2,7 @@
 基于 SUMO+TraCI 的 TD3 训练脚本
 使用 Stable-Baselines3 的 TD3 算法
 """
+import argparse
 import os
 import copy
 import yaml
@@ -17,9 +18,10 @@ from agents.td3_agent import TD3Agent
 from utils.config import get_agent_config, get_training_config
 from utils.logger import TrainingLogger
 from utils.plot import plot_training_curves
+from utils.run_dirs import add_run_dir_args, resolve_run_dirs
 
 
-def train_sumo_td3():
+def train_sumo_td3(log_dir=None, checkpoint_dir=None, run_timestamp=None):
     """SUMO 环境下的 TD3 训练主函数"""
     with open("config_sumo_vsl.yaml", "r", encoding="utf-8") as f:
         config = yaml.safe_load(f)
@@ -27,9 +29,12 @@ def train_sumo_td3():
     agent_config = get_agent_config(config, "td3")
     train_config = get_training_config(config)
 
-    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+    _, checkpoint_dir, log_dir = resolve_run_dirs(
-    checkpoint_dir = os.path.join("checkpoints", "td3", timestamp)
+        "td3",
-    log_dir = os.path.join("logs", "td3", timestamp)
+        log_dir=log_dir,
+        checkpoint_dir=checkpoint_dir,
+        run_timestamp=run_timestamp,
+    )
     os.makedirs(checkpoint_dir, exist_ok=True)
     os.makedirs(log_dir, exist_ok=True)
     runtime_config = copy.deepcopy(config)
@@ -193,4 +198,10 @@ def train_sumo_td3():
 
 
 if __name__ == "__main__":
-    train_sumo_td3()
+    parser = add_run_dir_args(argparse.ArgumentParser())
+    args = parser.parse_args()
+    train_sumo_td3(
+        log_dir=args.log_dir,
+        checkpoint_dir=args.checkpoint_dir,
+        run_timestamp=args.run_timestamp,
+    )

utils/run_dirs.py

@@ -0,0 +1,28 @@
+import argparse
+import os
+from datetime import datetime
+from typing import Optional, Tuple
+
+
+def add_run_dir_args(parser: argparse.ArgumentParser) -> argparse.ArgumentParser:
+    parser.add_argument("--log-dir", type=str, default=None, help="Training log output directory.")
+    parser.add_argument("--checkpoint-dir", type=str, default=None, help="Model checkpoint output directory.")
+    parser.add_argument("--run-timestamp", type=str, default=None, help="Run timestamp tag for default directories.")
+    return parser
+
+
+def resolve_run_dirs(
+    model_name: str,
+    log_dir: Optional[str] = None,
+    checkpoint_dir: Optional[str] = None,
+    run_timestamp: Optional[str] = None,
+) -> Tuple[str, str, str]:
+    """Resolve output directories from runtime args, falling back to per-model defaults."""
+    timestamp = run_timestamp or datetime.now().strftime("%Y%m%d_%H%M%S")
+
+    if checkpoint_dir is None:
+        checkpoint_dir = os.path.join("checkpoints", model_name, timestamp)
+    if log_dir is None:
+        log_dir = os.path.join("logs", model_name, timestamp)
+
+    return timestamp, checkpoint_dir, log_dir