def parse_args()

in experiments/train.py [0:0]

• 23 lines of code
• 1 McCabe index (conditional complexity)

def parse_args():
    parser = argparse.ArgumentParser("Reinforcement Learning experiments for multiagent environments")
    # Environment
    parser.add_argument("--scenario", type=str, default="simple", help="name of the scenario script")
    parser.add_argument("--max-episode-len", type=int, default=25, help="maximum episode length")
    parser.add_argument("--num-episodes", type=int, default=60000, help="number of episodes")
    parser.add_argument("--num-adversaries", type=int, default=0, help="number of adversaries")
    parser.add_argument("--good-policy", type=str, default="maddpg", help="policy for good agents")
    parser.add_argument("--adv-policy", type=str, default="maddpg", help="policy of adversaries")
    # Core training parameters
    parser.add_argument("--lr", type=float, default=1e-2, help="learning rate for Adam optimizer")
    parser.add_argument("--gamma", type=float, default=0.95, help="discount factor")
    parser.add_argument("--batch-size", type=int, default=1024, help="number of episodes to optimize at the same time")
    parser.add_argument("--num-units", type=int, default=64, help="number of units in the mlp")
    # Checkpointing
    parser.add_argument("--exp-name", type=str, default=None, help="name of the experiment")
    parser.add_argument("--save-dir", type=str, default="/tmp/policy/", help="directory in which training state and model should be saved")
    parser.add_argument("--save-rate", type=int, default=1000, help="save model once every time this many episodes are completed")
    parser.add_argument("--load-dir", type=str, default="", help="directory in which training state and model are loaded")
    # Evaluation
    parser.add_argument("--restore", action="store_true", default=False)
    parser.add_argument("--display", action="store_true", default=False)
    parser.add_argument("--benchmark", action="store_true", default=False)
    parser.add_argument("--benchmark-iters", type=int, default=100000, help="number of iterations run for benchmarking")
    parser.add_argument("--benchmark-dir", type=str, default="./benchmark_files/", help="directory where benchmark data is saved")
    parser.add_argument("--plots-dir", type=str, default="./learning_curves/", help="directory where plot data is saved")
    return parser.parse_args()