2 citations · 2 across the 3 of their papers we have counts for
3 papers
cs.LG2024★ 2 cited
MAMBA: an Effective World Model Approach for Meta-Reinforcement Learning
Zohar Rimon, Tom Jurgenson, Orr Krupnik +2
Meta-reinforcement learning (meta-RL) is a promising framework for tackling challenging domains requiring efficient exploration. Existing meta-RL algorithms are characterized by lo…
cs.LG2023
Fine-Tuning Generative Models as an Inference Method for Robotic Tasks
Orr Krupnik, Elisei Shafer, Tom Jurgenson +1
Adaptable models could greatly benefit robotic agents operating in the real world, allowing them to deal with novel and varying conditions. While approaches such as Bayesian infere…
cs.LG2023
Goal-Conditioned Supervised Learning with Sub-Goal Prediction
Tom Jurgenson, Aviv Tamar
Recently, a simple yet effective algorithm -- goal-conditioned supervised-learning (GCSL) -- was proposed to tackle goal-conditioned reinforcement-learning. GCSL is based on the pr…