98 citations · 127 across the 12 of their papers we have counts for
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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.LG2022
Hierarchies of Reward Machines
Daniel Furelos-Blanco, Mark Law, Anders Jonsson +2
Reward machines (RMs) are a recent formalism for representing the reward function of a reinforcement learning task through a finite-state machine whose edges encode subgoals of the…