3 citations · 3 across the 2 of their papers we have counts for
3 papers · 1 filter
Provably Efficient Exploration in Reward Machines with Low Regret
Hippolyte Bourel, Anders Jonsson, Odalric-Ambrym Maillard +2
We study reinforcement learning (RL) for decision processes with non-Markovian reward, in which high-level knowledge of the task in the form of reward machines is available to the…
Tightening Exploration in Upper Confidence Reinforcement Learning
Hippolyte Bourel, Odalric-Ambrym Maillard, Mohammad Sadegh Talebi
The upper confidence reinforcement learning (UCRL2) algorithm introduced in (Jaksch et al., 2010) is a popular method to perform regret minimization in unknown discrete Markov Deci…
Model-Based Reinforcement Learning Exploiting State-Action Equivalence
Mahsa Asadi, Mohammad Sadegh Talebi, Hippolyte Bourel +1
Leveraging an equivalence property in the state-space of a Markov Decision Process (MDP) has been investigated in several studies. This paper studies equivalence structure in the r…