Papers › GAN Q-learning

GAN Q-learning

13 May 2018arXiv:1805.04874archive 2025-07-28

Thang Doan, Bogdan Mazoure, Clare Lyle

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. We empirically show that our algorithm leverages the flexibility and blackbox approach of deep learning models while providing a viable alternative to traditional methods.

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Distributional Reinforcement Learning OpenAI Gym Q-Learning Reinforcement Learning Reinforcement Learning (RL)reinforcement-learning

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Convolution

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