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GANQL.md

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+# GAN Q-learning
+> Thang Doan, Bogdan Mazoure, Clare Lyle
+> McGill University
+
+## 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. 
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+We empirically show that our algorithm leverages the flexibility and blackbox approach of deep learning models while providing a viable alternative to traditional methods.