3 citations · 5 across the 6 of their papers we have counts for
6 papers
Sample Efficient Hierarchical Reinforcement Learning via Best Policy Identification
Anders Jonsson, Emilie Kaufmann, Gianmarco Tedeschi +1
We present HBPI-UCRL, a model-based algorithm for hierarchical reinforcement learning (HRL) that learns high-level and low-level policies in parallel. HBPI-UCRL exploits the fact t…
Learning The Minimum Action Distance
Lorenzo Steccanella, Joshua B. Evans, Özgür Şimşek +1
This paper presents a state representation framework for Markov decision processes (MDPs) that can be learned solely from state trajectories, requiring neither reward signals nor t…
Asymmetric Norms to Approximate the Minimum Action Distance
Lorenzo Steccanella, Anders Jonsson
This paper presents a state representation for reward-free Markov decision processes. The idea is to learn, in a self-supervised manner, an embedding space where distances between…
State Representation Learning for Goal-Conditioned Reinforcement Learning
Lorenzo Steccanella, Anders Jonsson
This paper presents a novel state representation for reward-free Markov decision processes. The idea is to learn, in a self-supervised manner, an embedding space where distances be…
Hierarchical Representation Learning for Markov Decision Processes
Lorenzo Steccanella, Simone Totaro, Anders Jonsson
In this paper we present a novel method for learning hierarchical representations of Markov decision processes. Our method works by partitioning the state space into subsets, and d…
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…