Reinforcement Learning in Feature Space: Matrix Bandit, Kernels, and Regret Bound

24 May 2019Lin F. YangMengdi Wang

Exploration in reinforcement learning (RL) suffers from the curse of dimensionality when the state-action space is large. A common practice is to parameterize the high-dimensional value and policy functions using given features... (read more)

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