1 citations · 1 across the 6 of their papers we have counts for
6 papers
Efficient Exploration at Scale
Seyed Mohammad Asghari, Chris Chute, Vikranth Dwaracherla +5
We develop an online learning algorithm that dramatically improves the data efficiency of reinforcement learning from human feedback (RLHF). Our algorithm incrementally updates rew…
Efficient Online Learning with Offline Datasets for Infinite Horizon MDPs: A Bayesian Approach
Dengwang Tang, Rahul Jain, Botao Hao +1
In this paper, we study the problem of efficient online reinforcement learning in the infinite horizon setting when there is an offline dataset to start with. We assume that the of…
Bridging Imitation and Online Reinforcement Learning: An Optimistic Tale
Botao Hao, Rahul Jain, Dengwang Tang +1
In this paper, we address the following problem: Given an offline demonstration dataset from an imperfect expert, what is the best way to leverage it to bootstrap online learning p…
Approximate Thompson Sampling via Epistemic Neural Networks
Ian Osband, Zheng Wen, Seyed Mohammad Asghari +4
Thompson sampling (TS) is a popular heuristic for action selection, but it requires sampling from a posterior distribution. Unfortunately, this can become computationally intractab…
Leveraging Demonstrations to Improve Online Learning: Quality Matters
Botao Hao, Rahul Jain, Tor Lattimore +2
We investigate the extent to which offline demonstration data can improve online learning. It is natural to expect some improvement, but the question is how, and by how much? We sh…
Robustness of Epinets against Distributional Shifts
Xiuyuan Lu, Ian Osband, Seyed Mohammad Asghari +4
Recent work introduced the epinet as a new approach to uncertainty modeling in deep learning. An epinet is a small neural network added to traditional neural networks, which, toget…