8 citations · 14 across the 2 of their papers we have counts for
3 papers
cs.AI2020★ 8 cited
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.AI2017
Learning Generalized Reactive Policies using Deep Neural Networks
Edward Groshev, Maxwell Goldstein, Aviv Tamar +2
We present a new approach to learning for planning, where knowledge acquired while solving a given set of planning problems is used to plan faster in related, but new problem insta…