JackM323
c5ca79a354
Training environment to make a walk model for the hexapod generated code that will be checked
40 lines
1.4 KiB
Python
40 lines
1.4 KiB
Python
"""Run a trained policy in the PyBullet sim for quick inspection.
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Usage:
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python ml/run_eval.py --model ml/checkpoints/ppo_joint_command.zip --episodes 3 --gui
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"""
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import argparse
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import sys
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from pathlib import Path
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ROOT_DIR = Path(__file__).resolve().parent.parent
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if str(ROOT_DIR) not in sys.path:
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sys.path.insert(0, str(ROOT_DIR))
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from ml.evaluate import evaluate
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def main():
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parser = argparse.ArgumentParser()
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parser.add_argument("--model", type=str, required=True, help="Path to the trained model file (.zip)")
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parser.add_argument("--episodes", type=int, default=3, help="Number of 'rounds' to run. One episode lasts from reset until the robot falls over or the time limit is reached.")
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parser.add_argument("--gui", action="store_true", help="Show the PyBullet GUI during evaluation")
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parser.add_argument("--num-robots", type=int, default=1, help="Number of robots in the evaluation environment")
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parser.add_argument("--robot-spacing", type=float, default=0.5, help="Spacing between robots in meters")
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parser.add_argument("--start-pose", type=str, choices=["init_deg", "init90_deg"], default="init_deg", help="Initial robot pose at reset")
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args = parser.parse_args()
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evaluate(
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model_path=args.model,
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episodes=args.episodes,
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use_gui=args.gui,
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num_robots=args.num_robots,
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robot_spacing=args.robot_spacing,
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start_pose=args.start_pose,
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)
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if __name__ == "__main__":
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main()
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