Compositional Reinforcement Learning for Discrete-Time Stochastic Control Systems

Abolfazl Lavaei, Mateo Perez, Milad Kazemi +4

We propose a compositional approach to synthesize policies for networks of continuous-space stochastic control systems with unknown dynamics using model-free reinforcement learning…