6 citations · 16 across the 14 of their papers we have counts for
3 papers · 1 filter
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…
Recursive Reinforcement Learning
Ernst Moritz Hahn, Mateo Perez, Sven Schewe +3
Recursion is the fundamental paradigm to finitely describe potentially infinite objects. As state-of-the-art reinforcement learning (RL) algorithms cannot directly reason about rec…
Alternating Good-for-MDP Automata
Ernst Moritz Hahn, Mateo Perez, Sven Schewe +3
When omega-regular objectives were first proposed in model-free reinforcement learning (RL) for controlling MDPs, deterministic Rabin automata were used in an attempt to provide a…