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GAN Q-learning
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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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Statistical Efficiency and Inference of Quantile Distributional Reinforcement Learning
Quantile fixed-point estimators of return distributions attain the parametric √n rate and the semiparametric efficiency bound for fixed and diverging numbers of quantiles, with a Berry–Esseen guarantee for smooth functionals.
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