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10 papers · 1 filter
The AI Economist: Optimal Economic Policy Design via Two-level Deep Reinforcement Learning
Stephan Zheng, Alexander Trott, Sunil Srinivasa +2
AI and reinforcement learning (RL) have improved many areas, but are not yet widely adopted in economic policy design, mechanism design, or economics at large. At the same time, cu…
WarpDrive: Extremely Fast End-to-End Deep Multi-Agent Reinforcement Learning on a GPU
Tian Lan, Sunil Srinivasa, Huan Wang +1
Deep reinforcement learning (RL) is a powerful framework to train decision-making models in complex environments. However, RL can be slow as it requires repeated interaction with a…
Exact Gap between Generalization Error and Uniform Convergence in Random Feature Models
Zitong Yang, Yu Bai, Song Mei
Recent work showed that there could be a large gap between the classical uniform convergence bound and the actual test error of zero-training-error predictors (interpolators) such…
Near-Optimal Offline Reinforcement Learning via Double Variance Reduction
Ming Yin, Yu Bai, Yu-Xiang Wang
We consider the problem of offline reinforcement learning (RL) -- a well-motivated setting of RL that aims at policy optimization using only historical data. Despite its wide appli…
Communication-Aware Collaborative Learning
Avrim Blum, Shelby Heinecke, Lev Reyzin
Algorithms for noiseless collaborative PAC learning have been analyzed and optimized in recent years with respect to sample complexity. In this paper, we study collaborative PAC le…
Profile Prediction: An Alignment-Based Pre-Training Task for Protein Sequence Models
Pascal Sturmfels, Jesse Vig, Ali Madani +1
For protein sequence datasets, unlabeled data has greatly outpaced labeled data due to the high cost of wet-lab characterization. Recent deep-learning approaches to protein predict…