Policy Gradient Methods for Distortion Risk Measures

Nithia Vijayan; Prashanth L. A

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arXiv · 2107.04422

Policy Gradient Methods for Distortion Risk Measures

Nithia Vijayan·Prashanth L. A

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Abstract

We propose policy gradient algorithms which learn risk-sensitive policies in a reinforcement learning (RL) framework. Our proposed algorithms maximize the distortion risk measure (DRM) of the cumulative reward in an episodic Markov decision process in on-policy and off-policy RL settings, respectively. We derive a variant of the policy gradient theorem that caters to the DRM objective, and integrate it with a likelihood ratio-based gradient estimation scheme. We derive non-asymptotic bounds that establish the convergence of our proposed algorithms to an approximate stationary point of the DRM objective.

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Nithia Vijayan, Prashanth L. A. 2021-07-09. Policy Gradient Methods for Distortion Risk Measures. https://arxiv.org/abs/2107.04422

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