Workshop on Programmatic Reinforcement Learning

This workshop explores using programmatic representations (e.g., code, symbolic programs, rules) to enhance agent learning and address key challenges in reinforcement learning (RL). By leveraging structured representations, we aim to improve interpretability, generalization, efficiency, and safety in deep RL, moving beyond the limitations of "black box" deep learning models.

The workshop brings together researchers in RL and program synthesis/code generation to discuss using programs as policies, reward functions, skill libraries, task generators, or environment models. This paradigm enables human-understandable reasoning, reduces reliance on massive data-driven models, and promotes modularity, fostering progress toward verifiable and robust agents across virtual and real-world applications.