Wasserstein Distance guided Adversarial Imitation Learning with Reward Shape Exploration

5 Jun 2020Ming ZhangYawei WangXiaoteng MaLi XiaJun YangZhiheng LiXiu Li

The generative adversarial imitation learning (GAIL) has provided an adversarial learning framework for imitating expert policy from demonstrations in high-dimensional continuous tasks. However, almost all GAIL and its extensions only design a kind of reward function of logarithmic form in the adversarial training strategy with the Jensen-Shannon (JS) divergence for all complex environments... (read more)

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