230 citations · 240 across the 8 of their papers we have counts for
10 papers
Entity-Centric Reinforcement Learning for Object Manipulation from Pixels
Dan Haramati, Tal Daniel, Aviv Tamar
Manipulating objects is a hallmark of human intelligence, and an important task in domains such as robotics. In principle, Reinforcement Learning (RL) offers a general approach to…
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…
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…
ContraBAR: Contrastive Bayes-Adaptive Deep RL
Era Choshen, Aviv Tamar
In meta reinforcement learning (meta RL), an agent seeks a Bayes-optimal policy -- the optimal policy when facing an unknown task that is sampled from some known task distribution.…
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…
A Deep Learning Perspective on Network Routing
Yarin Perry, Felipe Vieira Frujeri, Chaim Hoch +4
Routing is, arguably, the most fundamental task in computer networking, and the most extensively studied one. A key challenge for routing in real-world environments is the need to…