Adversarial Imitation via Variational Inverse Reinforcement Learning
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We consider a problem of learning a reward and policy from expert examples under unknown dynamics in high-dimensional scenarios. Our proposed method builds on the framework of generative adversarial networks and exploits reward shaping to learn near-optimal rewards and policies. Potential-based reward shaping functions are known to guide the learning agent whereas in this paper we bring forward their benefits in learning near-optimal rewards. Our method simultaneously learns a potential-based reward shaping function through variational information maximization along with the reward and policy under the adversarial learning formulation. We evaluate our method on various high-dimensional complex control tasks. We also evaluate our learned rewards in transfer learning problems where training and testing environments are made to be different from each other in terms of dynamics or structure. Our experimentation shows that our proposed method not only learns near-optimal rewards and policies matching expert behavior, but also performs significantly better than state-of-the-art inverse reinforcement learning algorithms.
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