3 citations · 6 across the 5 of their papers we have counts for
6 papers
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…
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…
Reward Shaping for Reinforcement Learning with Omega-Regular Objectives
E. M. Hahn, M. Perez, S. Schewe +3
Recently, successful approaches have been made to exploit good-for-MDPs automata (Büchi automata with a restricted form of nondeterminism) for model free reinforcement learning, a…
Good-for-MDPs Automata for Probabilistic Analysis and Reinforcement Learning
Ernst Moritz Hahn, Mateo Perez, Fabio Somenzi +3
We characterize the class of nondeterministic -automata that can be used for the analysis of finite Markov decision processes (MDPs). We call these automata `good-for-MDPs' (GFM…
Omega-Regular Objectives in Model-Free Reinforcement Learning
Ernst Moritz Hahn, Mateo Perez, Sven Schewe +3
We provide the first solution for model-free reinforcement learning of ω-regular objectives for Markov decision processes (MDPs). We present a constructive reduction from the almos…