98 citations · 127 across the 10 of their papers we have counts for
8 papers · 1 filter
Hierarchical reinforcement learning for efficient exploration and transfer
Lorenzo Steccanella, Simone Totaro, Damien Allonsius +1
Sparse-reward domains are challenging for reinforcement learning algorithms since significant exploration is needed before encountering reward for the first time. Hierarchical rein…
Improved Exploration in Factored Average-Reward MDPs
Mohammad Sadegh Talebi, Anders Jonsson, Odalric-Ambrym Maillard
We consider a regret minimization task under the average-reward criterion in an unknown Factored Markov Decision Process (FMDP). More specifically, we consider an FMDP where the st…
Induction and Exploitation of Subgoal Automata for Reinforcement Learning
Daniel Furelos-Blanco, Mark Law, Anders Jonsson +2
In this paper we present ISA, an approach for learning and exploiting subgoals in episodic reinforcement learning (RL) tasks. ISA interleaves reinforcement learning with the induct…
Fast active learning for pure exploration in reinforcement learning
Pierre Ménard, Omar Darwiche Domingues, Anders Jonsson +3
Realistic environments often provide agents with very limited feedback. When the environment is initially unknown, the feedback, in the beginning, can be completely absent, and the…
Planning in Markov Decision Processes with Gap-Dependent Sample Complexity
Anders Jonsson, Emilie Kaufmann, Pierre Ménard +3
We propose MDP-GapE, a new trajectory-based Monte-Carlo Tree Search algorithm for planning in a Markov Decision Process in which transitions have a finite support. We prove an uppe…
Adaptive Reward-Free Exploration
Emilie Kaufmann, Pierre Ménard, Omar Darwiche Domingues +3
Reward-free exploration is a reinforcement learning setting studied by Jin et al. (2020), who address it by running several algorithms with regret guarantees in parallel. In our wo…