39 lines
2.3 KiB
Python
39 lines
2.3 KiB
Python
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import argparse
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"""
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Here are the param for the training
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"""
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def get_args():
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parser = argparse.ArgumentParser("Reinforcement Learning experiments for multiagent environments")
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# Environment
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parser.add_argument("--scenario-name", type=str, default="simple_tag", help="name of the scenario script")
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parser.add_argument("--max-episode-len", type=int, default=100, help="maximum episode length")
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parser.add_argument("--time-steps", type=int, default=2000000, help="number of time steps")
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# agents
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parser.add_argument("--num-adversaries", type=int, default=1, help="number of adversaries")
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# Core training parameters
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parser.add_argument("--lr-actor", type=float, default=1e-4, help="learning rate of actor")
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parser.add_argument("--lr-critic", type=float, default=1e-3, help="learning rate of critic")
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parser.add_argument("--epsilon", type=float, default=0.1, help="epsilon greedy")
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parser.add_argument("--noise_rate", type=float, default=0.1, help="noise rate for sampling from a standard normal distribution ")
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parser.add_argument("--gamma", type=float, default=0.95, help="discount factor")
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parser.add_argument("--tau", type=float, default=0.01, help="parameter for updating the target network")
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parser.add_argument("--buffer-size", type=int, default=int(5e5), help="number of transitions can be stored in buffer")
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parser.add_argument("--batch-size", type=int, default=256, help="number of episodes to optimize at the same time")
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# Checkpointing
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parser.add_argument("--save-dir", type=str, default="./model", help="directory in which training state and model should be saved")
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parser.add_argument("--save-rate", type=int, default=2000, help="save model once every time this many episodes are completed")
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parser.add_argument("--model-dir", type=str, default="", help="directory in which training state and model are loaded")
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# Evaluate
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parser.add_argument("--evaluate-episodes", type=int, default=10, help="number of episodes for evaluating")
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parser.add_argument("--evaluate-episode-len", type=int, default=100, help="length of episodes for evaluating")
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parser.add_argument("--evaluate", type=bool, default=False, help="whether to evaluate the model")
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parser.add_argument("--evaluate-rate", type=int, default=1000, help="how often to evaluate model")
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args = parser.parse_args()
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return args
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