✨ initial commit

train.py

@@ -0,0 +1,47 @@
+import hydra
+from hydra.core.hydra_config import HydraConfig
+from omegaconf import DictConfig, OmegaConf
+
+from src.envs.cartpole import CartPoleEnv, CartPoleConfig
+from src.runners.mujoco import MuJoCoRunner, MuJoCoRunnerConfig
+from src.training.trainer import Trainer, TrainerConfig
+from src.core.env import ActuatorConfig
+
+
+def _build_env_config(cfg: DictConfig) -> CartPoleConfig:
+    env_dict = OmegaConf.to_container(cfg.env, resolve=True)
+    if "actuators" in env_dict:
+        for a in env_dict["actuators"]:
+            if "ctrl_range" in a:
+                a["ctrl_range"] = tuple(a["ctrl_range"])
+        env_dict["actuators"] = [ActuatorConfig(**a) for a in env_dict["actuators"]]
+    return CartPoleConfig(**env_dict)
+
+
+@hydra.main(version_base=None, config_path="configs", config_name="config")
+def main(cfg: DictConfig) -> None:
+    env_config = _build_env_config(cfg)
+    runner_config = MuJoCoRunnerConfig(**OmegaConf.to_container(cfg.runner, resolve=True))
+
+    training_dict = OmegaConf.to_container(cfg.training, resolve=True)
+    # Build ClearML task name dynamically from Hydra config group choices
+    if not training_dict.get("clearml_task"):
+        choices = HydraConfig.get().runtime.choices
+        env_name = choices.get("env", "env")
+        runner_name = choices.get("runner", "runner")
+        training_name = choices.get("training", "algo")
+        training_dict["clearml_task"] = f"{env_name}-{runner_name}-{training_name}"
+    trainer_config = TrainerConfig(**training_dict)
+
+    env = CartPoleEnv(env_config)
+    runner = MuJoCoRunner(env=env, config=runner_config)
+    trainer = Trainer(runner=runner, config=trainer_config)
+
+    try:
+        trainer.train()
+    finally:
+        trainer.close()
+
+
+if __name__ == "__main__":
+    main()