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Collaborating Authors

 Hoang Le



Imitation-Projected Programmatic Reinforcement Learning

Neural Information Processing Systems

We study the problem of programmatic reinforcement learning, in which policies are represented as short programs in a symbolic language. Programmatic policies can be more interpretable, generalizable, and amenable to formal verification than neural policies; however, designing rigorous learning approaches for such policies remains a challenge.