3 citations · 3 across the 2 of their papers we have counts for
2 papers
cs.LG2022
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…
cs.LG2020★ 3 cited
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…