45 citations · 67 across the 18 of their papers we have counts for
7 papers · 1 filter
Assume-Guarantee Reinforcement Learning
Milad Kazemi, Mateo Perez, Fabio Somenzi +3
We present a modular approach to \emph{reinforcement learning} (RL) in environments consisting of simpler components evolving in parallel. A monolithic view of such modular environ…
A PAC Learning Algorithm for LTL and Omega-regular Objectives in MDPs
Mateo Perez, Fabio Somenzi, Ashutosh Trivedi
Linear temporal logic (LTL) and omega-regular objectives -- a superset of LTL -- have seen recent use as a way to express non-Markovian objectives in reinforcement learning. We int…
Omega-Regular Reward Machines
Ernst Moritz Hahn, Mateo Perez, Sven Schewe +3
Reinforcement learning (RL) is a powerful approach for training agents to perform tasks, but designing an appropriate reward mechanism is critical to its success. However, in many…
Reinforcement Learning for Omega-Regular Specifications on Continuous-Time MDP
Amin Falah, Shibashis Guha, Ashutosh Trivedi
Continuous-time Markov decision processes (CTMDPs) are canonical models to express sequential decision-making under dense-time and stochastic environments. When the stochastic evol…
Mungojerrie: Reinforcement Learning of Linear-Time Objectives
Ernst Moritz Hahn, Mateo Perez, Sven Schewe +3
Reinforcement learning synthesizes controllers without prior knowledge of the system. At each timestep, a reward is given. The controllers optimize the discounted sum of these rewa…
Model-free Reinforcement Learning for Branching Markov Decision Processes
Ernst Moritz Hahn, Mateo Perez, Sven Schewe +3
We study reinforcement learning for the optimal control of Branching Markov Decision Processes (BMDPs), a natural extension of (multitype) Branching Markov Chains (BMCs). The state…