Dynamic probabilistic logic models for effective abstractions in RL

• URL: http://arxiv.org/abs/2110.08318v1
• Date: Fri, 15 Oct 2021 18:53:04 GMT
• Title: Dynamic probabilistic logic models for effective abstractions in RL
• Authors: Harsha Kokel, Arjun Manoharan, Sriraam Natarajan, Balaraman Ravindran, Prasad Tadepalli
• Abstract summary: RePReL is a hierarchical framework that leverages a relational planner to provide useful state abstractions for learning. 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.
• Score: 35.54018388244684
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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