1k citations · 2k across the 65 of their papers we have counts for
7 papers · 1 filter
Internet Congestion Control via Deep Reinforcement Learning
Nathan Jay, Noga H. Rotman, P. Brighten Godfrey +2
We present and investigate a novel and timely application domain for deep reinforcement learning (RL): Internet congestion control. Congestion control is the core networking task o…
Learning and Planning with a Semantic Model
Yi Wu, Yuxin Wu, Aviv Tamar +3
Building deep reinforcement learning agents that can generalize and adapt to unseen environments remains a fundamental challenge for AI. This paper describes progresses on this cha…
Distributional Multivariate Policy Evaluation and Exploration with the Bellman GAN
Dror Freirich, Ron Meir, Aviv Tamar
The recently proposed distributional approach to reinforcement learning (DiRL) is centered on learning the distribution of the reward-to-go, often referred to as the value distribu…
Learning Plannable Representations with Causal InfoGAN
Thanard Kurutach, Aviv Tamar, Ge Yang +2
In recent years, deep generative models have been shown to 'imagine' convincing high-dimensional observations such as images, audio, and even video, learning directly from raw data…
Constrained Policy Improvement for Safe and Efficient Reinforcement Learning
Elad Sarafian, Aviv Tamar, Sarit Kraus
We propose a policy improvement algorithm for Reinforcement Learning (RL) which is called Rerouted Behavior Improvement (RBI). RBI is designed to take into account the evaluation e…
Learning Robotic Assembly from CAD
Garrett Thomas, Melissa Chien, Aviv Tamar +2
In this work, motivated by recent manufacturing trends, we investigate autonomous robotic assembly. Industrial assembly tasks require contact-rich manipulation skills, which are ch…
Model-Ensemble Trust-Region Policy Optimization
Thanard Kurutach, Ignasi Clavera, Yan Duan +2
Model-free reinforcement learning (RL) methods are succeeding in a growing number of tasks, aided by recent advances in deep learning. However, they tend to suffer from high sample…