Zooming for Efficient Model-Free Reinforcement Learning in Metric Spaces

9 Mar 2020Ahmed TouatiAdrien Ali TaigaMarc G. Bellemare

Despite the wealth of research into provably efficient reinforcement learning algorithms, most works focus on tabular representation and thus struggle to handle exponentially or infinitely large state-action spaces. In this paper, we consider episodic reinforcement learning with a continuous state-action space which is assumed to be equipped with a natural metric that characterizes the proximity between different states and actions... (read more)

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