42 citations · 185 across the 35 of their papers we have counts for
6 papers · 1 filter
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…
MC-NN: An End-to-End Multi-Channel Neural Network Approach for Predicting Influenza A Virus Hosts and Antigenic Types
Yanhua Xu, Dominik Wojtczak
Influenza poses a significant threat to public health, particularly among the elderly, young children, and people with underlying dis-eases. The manifestation of severe conditions,…
Dive into Machine Learning Algorithms for Influenza Virus Host Prediction with Hemagglutinin Sequences
Yanhua Xu, Dominik Wojtczak
Influenza viruses mutate rapidly and can pose a threat to public health, especially to those in vulnerable groups. Throughout history, influenza A viruses have caused pandemics bet…
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…
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…