Title: Rethinking Learning Dynamics in RL using Adversarial Networks. Abstract: Recent years have seen tremendous progress in methods of reinforcement learning. However, most of these approaches have been trained in a straightforward fashion and are generally not robust to adversity, especially in the meta-RL setting. To the best of our knowledge, our work is the first to propose an adversarial training regime for Multi-Task Reinforcement Learning, which requires no manual intervention or domain knowledge of the environments. Our experiments on multiple environments in the Multi-Task Reinforcement learning domain demonstrate that the adversarial process leads to a better exploration of numerous solutions and a deeper understanding of the environment. We also adapt existing measures of causal attribution to draw insights from the skills learned, facilitating easier re-purposing of skills for adaptation to unseen environments and tasks.
