Hoang Le
Imitation-Projected Programmatic Reinforcement Learning
Abhinav Verma, Hoang Le, Yisong Yue, Swarat Chaudhuri
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.