activity
20192026
most citedSub-Goal Trees -- a Framework for Goal-Based Reinforcement Learning

8 citations · 16 across the 9 of their papers we have counts for

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Showing 2024Show all

2 papers · 1 filter

cs.RO2024

RoboArm-NMP: a Learning Environment for Neural Motion Planning

Tom Jurgenson, Matan Sudry, Gal Avineri +1

We present RoboArm-NMP, a learning and evaluation environment that allows simple and thorough evaluations of Neural Motion Planning (NMP) algorithms, focused on robotic manipulator…

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…