RePReL: Integrating Relational Planning and Reinforcement Learning for Effective Abstraction
Keywords:Reinforcement Learning Using Planning (model-based, Bayesian, Deep, Etc.), Theoretical Aspects Of Planning And Learning, Applications That Involve A Combination Of Learning With Planning Or Scheduling, Learning To Improve The Effectiveness Of Planning & Scheduling Systems
AbstractState abstraction is necessary for better task transfer in complex reinforcement learning environments. Inspired by the benefit of state abstraction in MAXQ and building upon hybrid planner-RL architectures, we propose RePReL, a hierarchical framework that leverages a relational planner to provide useful state abstractions. Our experiments demonstrate that the abstractions enable faster learning and efficient transfer across tasks. More importantly, our framework enables the application of standard RL approaches for learning in structured domains. The benefit of using the state abstractions is critical in relational settings, where the number and/or types of objects are not fixed apriori. Our experiments clearly show that RePReL framework not only achieves better performance and efficient learning on the task at hand but also demonstrates better generalization to unseen tasks.
How to Cite
Kokel, H., Manoharan, A., Natarajan, S., Ravindran, B., & Tadepalli, P. (2021). RePReL: Integrating Relational Planning and Reinforcement Learning for Effective Abstraction. Proceedings of the International Conference on Automated Planning and Scheduling, 31(1), 533-541. https://doi.org/10.1609/icaps.v31i1.16001
Special Track on Planning and Learning