Reinforcement Learning Under Probabilistic Spatio-Temporal Constraints with Time Windows

• URL: http://arxiv.org/abs/2307.15910v1
• Date: Sat, 29 Jul 2023 06:47:14 GMT
• Title: Reinforcement Learning Under Probabilistic Spatio-Temporal Constraints with Time Windows
• Authors: Xiaoshan Lin, Abbasali Koochakzadeh, Yasin Yazicioglu, Derya Aksaray
• Abstract summary: We propose an automata-theoretic approach for reinforcement learning (RL) under complex-temporal constraints with time windows. We provide theoretical guarantees on the resulting probability of constraint satisfaction. We also provide numerical results in a scenario where a robot explores the environment to discover high-reward regions.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose an automata-theoretic approach for reinforcement learning (RL) under complex spatio-temporal constraints with time windows. The problem is formulated using a Markov decision process under a bounded temporal logic constraint. Different from existing RL methods that can eventually learn optimal policies satisfying such constraints, our proposed approach enforces a desired probability of constraint satisfaction throughout learning. This is achieved by translating the bounded temporal logic constraint into a total automaton and avoiding "unsafe" actions based on the available prior information regarding the transition probabilities, i.e., a pair of upper and lower bounds for each transition probability. We provide theoretical guarantees on the resulting probability of constraint satisfaction. We also provide numerical results in a scenario where a robot explores the environment to discover high-reward regions while fulfilling some periodic pick-up and delivery tasks that are encoded as temporal logic constraints.

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