Learning to Control in Metric Space with Optimal Regret

5 May 2019 Lin F. Yang Chengzhuo Ni Mengdi Wang

We study online reinforcement learning for finite-horizon deterministic control systems with {\it arbitrary} state and action spaces. Suppose that the transition dynamics and reward function is unknown, but the state and action space is endowed with a metric that characterizes the proximity between different states and actions... (read more)

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