Rethinking Learning Dynamics in RL using Adversarial Networks

• URL: http://arxiv.org/abs/2201.11783v1
• Date: Thu, 27 Jan 2022 19:51:09 GMT
• Title: Rethinking Learning Dynamics in RL using Adversarial Networks
• Authors: Ramnath Kumar, Tristan Deleu and Yoshua Bengio
• Abstract summary: We present a learning mechanism for reinforcement learning of closely related skills parameterized via a skill embedding space. The main contribution of our work is to formulate an adversarial training regime for reinforcement learning with the help of entropy-regularized policy gradient formulation.
• Score: 79.56118674435844
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present a learning mechanism for reinforcement learning of closely related skills parameterized via a skill embedding space. Our approach is grounded on the intuition that nothing makes you learn better than a coevolving adversary. The main contribution of our work is to formulate an adversarial training regime for reinforcement learning with the help of entropy-regularized policy gradient formulation. We also adapt existing measures of causal attribution to draw insights from the skills learned. Our experiments demonstrate that the adversarial process leads to a better exploration of multiple solutions and understanding the minimum number of different skills necessary to solve a given set of tasks.

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