Web: http://arxiv.org/abs/2206.11004

June 23, 2022, 1:10 a.m. | Kaifeng Zhang, Rui Zhao, Ziming Zhang, Yang Gao

cs.LG updates on arXiv.org arxiv.org

Reinforcement learning (RL) provides a powerful framework for
decision-making, but its application in practice often requires a carefully
designed reward function. Adversarial Imitation Learning (AIL) sheds light on
automatic policy acquisition without access to the reward signal from the
environment. In this work, we propose Auto-Encoding Adversarial Imitation
Learning (AEAIL), a robust and scalable AIL framework. To induce expert
policies from demonstrations, AEAIL utilizes the reconstruction error of an
auto-encoder as a reward signal, which provides more information for optimizing …

arxiv encoding imitation learning learning lg

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