Policy Gradient in Robust MDPs with Global Convergence Guarantee

• URL: http://arxiv.org/abs/2212.10439v2
• Date: Wed, 7 Jun 2023 12:20:17 GMT
• Title: Policy Gradient in Robust MDPs with Global Convergence Guarantee
• Authors: Qiuhao Wang, Chin Pang Ho, Marek Petrik
• Abstract summary: Robust Markov decision processes (RMDPs) provide a promising framework for computing reliable policies in the face of model errors. This paper proposes a new Double-Loop Robust Policy Gradient (DRPG), the first generic policy gradient method for RMDPs. In contrast with prior robust policy gradient algorithms, DRPG monotonically reduces approximation errors to guarantee convergence to a globally optimal policy.
• Score: 13.40471012593073
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Robust Markov decision processes (RMDPs) provide a promising framework for computing reliable policies in the face of model errors. Many successful reinforcement learning algorithms build on variations of policy-gradient methods, but adapting these methods to RMDPs has been challenging. As a result, the applicability of RMDPs to large, practical domains remains limited. This paper proposes a new Double-Loop Robust Policy Gradient (DRPG), the first generic policy gradient method for RMDPs. In contrast with prior robust policy gradient algorithms, DRPG monotonically reduces approximation errors to guarantee convergence to a globally optimal policy in tabular RMDPs. We introduce a novel parametric transition kernel and solve the inner loop robust policy via a gradient-based method. Finally, our numerical results demonstrate the utility of our new algorithm and confirm its global convergence properties.

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