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arXiv · 2310.11678

Using Experience Classification for Training Non-Markovian Tasks

Ruixuan Miao·Xu Lu·Cong Tian·Bin Yu·Zhenhua Duan
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Abstract

Unlike the standard Reinforcement Learning (RL) model, many real-world tasks are non-Markovian, whose rewards are predicated on state history rather than solely on the current state. Solving a non-Markovian task, frequently applied in practical applications such as autonomous driving, financial trading, and medical diagnosis, can be quite challenging. We propose a novel RL approach to achieve non-Markovian rewards expressed in temporal logic LTL$_f$ (Linear Temporal Logic over Finite Traces). To this end, an encoding of linear complexity from LTL$_f$ into MDPs (Markov Decision Processes) is introduced to take advantage of advanced RL algorithms. Then, a prioritized experience replay technique based on the automata structure (semantics equivalent to LTL$_f$ specification) is utilized to improve the training process. We empirically evaluate several benchmark problems augmented with non-Markovian tasks to demonstrate the feasibility and effectiveness of our approach.

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Ruixuan Miao, Xu Lu, Cong Tian, Bin Yu, Zhenhua Duan. 2023-10-18. Using Experience Classification for Training Non-Markovian Tasks. https://arxiv.org/abs/2310.11678

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