Theoretical foundations for programmatic reinforcement learning

arXiv:2402.11650v1 Announce Type: new
Abstract: The field of Reinforcement Learning (RL) is concerned with algorithms for learning optimal policies in unknown stochastic environments. Programmatic RL studies representations of policies as programs, meaning involving higher order constructs such as control loops. Despite attracting a lot of attention at the intersection of the machine learning and formal methods communities, very little is known on the theoretical front about programmatic RL: what are good classes of programmatic policies? How large are optimal programmatic …

abstract algorithms arxiv attention control cs.lg cs.lo cs.pl environments intersection machine machine learning meaning programmatic reinforcement reinforcement learning stochastic studies type