On Practical Robust Reinforcement Learning: Practical Uncertainty Set and Double-Agent Algorithm

• URL: http://arxiv.org/abs/2305.06657v3
• Date: Mon, 20 Nov 2023 03:39:38 GMT
• Title: On Practical Robust Reinforcement Learning: Practical Uncertainty Set and Double-Agent Algorithm
• Authors: Ukjo Hwang, Songnam Hong
• Abstract summary: Robust reinforcement learning (RRL) aims at seeking a robust policy to optimize the worst case performance over an uncertainty set of Markov decision processes (MDPs)
• Score: 11.748284119769039
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Robust reinforcement learning (RRL) aims at seeking a robust policy to optimize the worst case performance over an uncertainty set of Markov decision processes (MDPs). This set contains some perturbed MDPs from a nominal MDP (N-MDP) that generate samples for training, which reflects some potential mismatches between training (i.e., N-MDP) and true environments. In this paper we present an elaborated uncertainty set by excluding some implausible MDPs from the existing sets. Under this uncertainty set, we develop a sample-based RRL algorithm (named ARQ-Learning) for tabular setting and characterize its finite-time error bound. Also, it is proved that ARQ-Learning converges as fast as the standard Q-Learning and robust Q-Learning while ensuring better robustness. We introduce an additional pessimistic agent which can tackle the major bottleneck for the extension of ARQ-Learning into the cases with larger or continuous state spaces. Incorporating this idea into RL algorithms, we propose double-agent algorithms for model-free RRL. Via experiments, we demonstrate the effectiveness of the proposed algorithms.

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