LCRL: Certified Policy Synthesis via Logically-Constrained Reinforcement Learning. (arXiv:2209.10341v1 [cs.LG])

LCRL is a software tool that implements model-free Reinforcement Learning
(RL) algorithms over unknown Markov Decision Processes (MDPs), synthesising
policies that satisfy a given linear temporal specification with maximal
probability. LCRL leverages partially deterministic finite-state machines known
as Limit Deterministic Buchi Automata (LDBA) to express a given linear temporal
specification. A reward function for the RL algorithm is shaped on-the-fly,
based on the structure of the LDBA. Theoretical guarantees under proper
assumptions ensure the convergence of the RL algorithm to …

arxiv policy reinforcement reinforcement learning