no code implementations • 9 Sep 2023 • Cyrus Neary, Aryaman Singh Samyal, Christos Verginis, Murat Cubuktepe, Ufuk Topcu
We propose a framework for verifiable and compositional reinforcement learning (RL) in which a collection of RL subsystems, each of which learns to accomplish a separate subtask, are composed to achieve an overall task.
no code implementations • 2 Mar 2023 • Christos Verginis
Control and planning of multi-agent systems is an active and increasingly studied topic of research, with many practical applications such as rescue missions, security, surveillance, and transportation.
no code implementations • 20 Apr 2022 • Christos Verginis, Cevahir Koprulu, Sandeep Chinchali, Ufuk Topcu
We develop a reinforcement-learning algorithm that infers a reward machine that encodes the underlying task while learning how to execute it, despite the uncertainties of the propositions' truth values.
1 code implementation • 7 Jun 2021 • Cyrus Neary, Christos Verginis, Murat Cubuktepe, Ufuk Topcu
We propose a framework for verifiable and compositional reinforcement learning (RL) in which a collection of RL subsystems, each of which learns to accomplish a separate subtask, are composed to achieve an overall task.