Model-Free Robust $φ$-Divergence Reinforcement Learning Using Both Offline and Online Data

• URL: http://arxiv.org/abs/2405.05468v1
• Date: Wed, 8 May 2024 23:52:37 GMT
• Title: Model-Free Robust $φ$-Divergence Reinforcement Learning Using Both Offline and Online Data
• Authors: Kishan Panaganti, Adam Wierman, Eric Mazumdar,
• Abstract summary: We propose a model-free algorithm called Robust $phi$-regularized fitted Q-iteration (RPQ) for learning an $epsilon$-optimal robust policy. We also introduce the hybrid robust $phi$-regularized reinforcement learning framework to learn an optimal robust policy using both historical data and online sampling.
• Score: 16.995406965407003
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The robust $\phi$-regularized Markov Decision Process (RRMDP) framework focuses on designing control policies that are robust against parameter uncertainties due to mismatches between the simulator (nominal) model and real-world settings. This work makes two important contributions. First, we propose a model-free algorithm called Robust $\phi$-regularized fitted Q-iteration (RPQ) for learning an $\epsilon$-optimal robust policy that uses only the historical data collected by rolling out a behavior policy (with robust exploratory requirement) on the nominal model. To the best of our knowledge, we provide the first unified analysis for a class of $\phi$-divergences achieving robust optimal policies in high-dimensional systems with general function approximation. Second, we introduce the hybrid robust $\phi$-regularized reinforcement learning framework to learn an optimal robust policy using both historical data and online sampling. Towards this framework, we propose a model-free algorithm called Hybrid robust Total-variation-regularized Q-iteration (HyTQ: pronounced height-Q). To the best of our knowledge, we provide the first improved out-of-data-distribution assumption in large-scale problems with general function approximation under the hybrid robust $\phi$-regularized reinforcement learning framework. Finally, we provide theoretical guarantees on the performance of the learned policies of our algorithms on systems with arbitrary large state space.

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