research
∙
07/11/2023
Scaling Distributed Multi-task Reinforcement Learning with Experience Sharing
Recently, DARPA launched the ShELL program, which aims to explore how ex...
share
research
∙
06/01/2022
Provably Efficient Lifelong Reinforcement Learning with Linear Function Approximation
We study lifelong reinforcement learning (RL) in a regret minimization s...
research
∙
05/26/2022
Distributed Contextual Linear Bandits with Minimax Optimal Communication Cost
We study distributed contextual linear bandits with stochastic contexts,...
research
∙
06/11/2021
Safe Reinforcement Learning with Linear Function Approximation
Safety in reinforcement learning has become increasingly important in re...
research
∙
03/21/2021
UCB-based Algorithms for Multinomial Logistic Regression Bandits
Out of the rich family of generalized linear bandits, perhaps the most w...
research
∙
12/01/2020
Decentralized Multi-Agent Linear Bandits with Safety Constraints
We study decentralized stochastic linear bandits, where a network of N a...
research
∙
05/05/2020
Regret Bounds for Safe Gaussian Process Bandit Optimization
Many applications require a learner to make sequential decisions given u...
research
∙
11/06/2019
Safe Linear Thompson Sampling
The design and performance analysis of bandit algorithms in the presence...
research
∙
08/16/2019
