Using Experience Classification for Training Non-Markovian Tasks

• URL: http://arxiv.org/abs/2310.11678v1
• Date: Wed, 18 Oct 2023 03:00:59 GMT
• Title: Using Experience Classification for Training Non-Markovian Tasks
• Authors: Ruixuan Miao, Xu Lu, Cong Tian, Bin Yu, Zhenhua Duan
• Abstract summary: Non-Markovian tasks are frequently applied in practical applications such as autonomous driving, financial trading, and medical diagnosis. We propose a novel RL approach to achieve non-Markovian rewards expressed in temporal logic$_f$.
• Score: 11.267797018727402
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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