Title: BPRL: A Behavioral Approach to State Representation in Reinforcement Learning
Primary area: reinforcement learning
Keywords: Reinforcement Learning, Behavioral Programming, Symbolic Representation, Representation Learning

Abstract: Deep Reinforcement Learning (DRL) often suffers from poor sample efficiency and limited generalization in complex, high-dimensional environments. A key challenge is designing effective state representations, which typically requires manual, domain-specific feature engineering. We propose Behavioral Programming Reinforcement Learning (BPRL), a framework that automates the construction of compact, semantically rich state representations. BPRL leverages Behavioral Programming (BP)$-$a scenario-based modeling paradigm$-$to specify the environment's dynamics. The core contribution is that the very same BP model used to define the environment’s logic is also used to $\textit{automatically derive}$ the state representation for the DRL agent. This dual use of the BP model eliminates the need for manual feature design while ensuring that the extracted representations capture both high-level symbolic structure and temporal dependencies. By combining BP's modularity with structured observations, BPRL simplifies environment modeling and enhances agent learning. Experiments across multiple DRL algorithms on MiniGrid benchmarks demonstrate that BPRL substantially improves sample efficiency and asymptotic performance over standard baselines.

TL;DR: We introduce BPRL, a framework that automatically derives structured state representations from environment logic using Behavioral Programming to enhance reinforcement learning performance.