84 lines
2.2 KiB
Python
84 lines
2.2 KiB
Python
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import argparse
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import os
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from glob import glob
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import gymnasium as gym
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from gymnasium.envs.registration import register
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from sb3_contrib import TQC
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from stable_baselines3 import A2C, PPO, SAC, TD3
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register(
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id="BDX_env",
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entry_point="env_humanoid:BDXEnv",
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autoreset=True,
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# max_episode_steps=200,
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)
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def test(env, sb3_algo, path_to_model):
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if not path_to_model.endswith(".zip"):
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models_paths = glob(path_to_model + "/*.zip")
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latest_model_id = 0
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latest_model_path = None
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for model_path in models_paths:
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model_id = model_path.split("/")[-1][: -len(".zip")].split("_")[-1]
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if int(model_id) > latest_model_id:
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latest_model_id = int(model_id)
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latest_model_path = model_path
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if latest_model_path is None:
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print("No models found in directory: ", path_to_model)
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return
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print("Using latest model: ", latest_model_path)
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path_to_model = latest_model_path
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match sb3_algo:
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case "SAC":
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model = SAC.load(path_to_model, env=env)
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case "TD3":
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model = TD3.load(path_to_model, env=env)
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case "A2C":
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model = A2C.load(path_to_model, env=env)
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case "TQC":
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model = TQC.load(path_to_model, env=env)
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case "PPO":
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model = PPO("MlpPolicy", env)
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model.policy.load(path_to_model)
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# model = PPO.load(path_to_model, env=env)
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case _:
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print("Algorithm not found")
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return
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obs = env.reset()[0]
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done = False
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extra_steps = 500
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while True:
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action, _ = model.predict(obs)
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obs, _, done, _, _ = env.step(action)
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if done:
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extra_steps -= 1
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if extra_steps < 0:
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break
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(description="Test model")
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parser.add_argument(
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"-p",
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"--path",
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metavar="path_to_model",
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help="Path to the model. If directory, will use the latest model.",
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)
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parser.add_argument("-a", "--algo", default="SAC")
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args = parser.parse_args()
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gymenv = gym.make("BDX_env", render_mode="human")
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test(gymenv, args.algo, path_to_model=args.path)
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