Dynamic probabilistic logic models for effective abstractions in RL

Harsha Kokel; Arjun Manoharan; Sriraam Natarajan; Balaraman Ravindran; Prasad Tadepalli

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arXiv · 2110.08318

Dynamic probabilistic logic models for effective abstractions in RL

Harsha Kokel·Arjun Manoharan·Sriraam Natarajan·Balaraman Ravindran·Prasad Tadepalli

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Abstract

State abstraction enables sample-efficient learning and better task transfer in complex reinforcement learning environments. Recently, we proposed RePReL (Kokel et al. 2021), a hierarchical framework that leverages a relational planner to provide useful state abstractions for learning. We present a brief overview of this framework and the use of a dynamic probabilistic logic model to design these state abstractions. Our experiments show that RePReL not only achieves better performance and efficient learning on the task at hand but also demonstrates better generalization to unseen tasks.

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Harsha Kokel, Arjun Manoharan, Sriraam Natarajan, Balaraman Ravindran, Prasad Tadepalli. 2021-10-15. Dynamic probabilistic logic models for effective abstractions in RL. https://arxiv.org/abs/2110.08318

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