Contrastive Reinforcement Learning for Symbolic Planning

Symbolic planning is a type of sequential decision-making problem for goal reaching modeled in a symbolic language like Planning Domain Definition Language (PDDL). The mainstream approaches for solving symbolic planning problems are search-based methods. Recently, along with the development of machine learning techniques, there is a growing interest in exploiting learning-based approaches in symbolic planning. In spite of the similarity between symbolic planning and Markov Decision Processes, most learning-based approaches for symbolic planning are supervised (imitation) learning methods. Model-free reinforcement learning methods, which can explore the environment freely, are under-explored in this area. In this paper, we study how to customize contrastive reinforcement learning (CRL) for symbolic planning, which results in a model-free approach for symbolic planning with competitive performance.