GAN Q-learning

H/T: Vincent Boucher

Abstract : “Distributional reinforcement learning (distributional RL) has seen empirical success in complex Markov Decision Processes (MDPs) in the setting of nonlinear function approximation. However there are many different ways in which one can leverage the distributional approach to reinforcement learning. In this paper, we propose GAN Q-learning, a novel distributional RL method based on generative adversarial networks (GANs) and analyze its performance in simple tabular environments, as well as OpenAI Gym (…).”

By Thang Doan, Bogdan Mazoure, Clare Lyle : https://arxiv.org/abs/1805.04874


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