most citedSub-Goal Trees -- a Framework for Goal-Directed Trajectory Prediction and Optimization

6 citations · 6 across the 1 of their papers we have counts for

collaborators

3 papers

cs.AI2020

Sub-Goal Trees -- a Framework for Goal-Based Reinforcement Learning

Tom Jurgenson, Or Avner, Edward Groshev +1

Many AI problems, in robotics and other domains, are goal-based, essentially seeking trajectories leading to various goal states. Reinforcement learning (RL), building on Bellman's…

cs.LG2019★ 6 cited

Sub-Goal Trees -- a Framework for Goal-Directed Trajectory Prediction and Optimization

Tom Jurgenson, Edward Groshev, Aviv Tamar

Many AI problems, in robotics and other domains, are goal-directed, essentially seeking a trajectory leading to some goal state. In such problems, the way we choose to represent a…

cs.RO2019

Harnessing Reinforcement Learning for Neural Motion Planning

Tom Jurgenson, Aviv Tamar

Motion planning is an essential component in most of today's robotic applications. In this work, we consider the learning setting, where a set of solved motion planning problems is…