Choquet regularization for reinforcement learning

Xia Han; Ruodu Wang; Xun Yu Zhou

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arXiv · 2208.08497

Choquet regularization for reinforcement learning

Xia Han·Ruodu Wang·Xun Yu Zhou

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Abstract

We propose \emph{Choquet regularizers} to measure and manage the level of exploration for reinforcement learning (RL), and reformulate the continuous-time entropy-regularized RL problem of Wang et al. (2020, JMLR, 21(198)) in which we replace the differential entropy used for regularization with a Choquet regularizer. We derive the Hamilton--Jacobi--Bellman equation of the problem, and solve it explicitly in the linear--quadratic (LQ) case via maximizing statically a mean--variance constrained Choquet regularizer. Under the LQ setting, we derive explicit optimal distributions for several specific Choquet regularizers, and conversely identify the Choquet regularizers that generate a number of broadly used exploratory samplers such as $\epsilon$-greedy, exponential, uniform and Gaussian.

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stat.ML cs.LG q-fin.MF

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BibTeX RIS

Xia Han, Ruodu Wang, Xun Yu Zhou. 2022-08-17. Choquet regularization for reinforcement learning. https://arxiv.org/abs/2208.08497

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