One Policy is Enough: Parallel Exploration with a Single Policy is Near-Optimal for Reward-Free Reinforcement Learning

31 May 2022

Papers citing "One Policy is Enough: Parallel Exploration with a Single Policy is Near-Optimal for Reward-Free Reinforcement Learning"

6 / 6 papers shown

Title
Harnessing the Power of Federated Learning in Federated Contextual Bandits Chengshuai Shi Ruida Zhou Kun Yang Cong Shen FedML 21 0 0 26 Dec 2023
Transfer in Reinforcement Learning via Regret Bounds for Learning Agents Adrienne Tuynman R. Ortner 19 2 0 02 Feb 2022
Provably Efficient Policy Optimization for Two-Player Zero-Sum Markov Games Yulai Zhao Yuandong Tian Jason D. Lee S. Du OffRL 41 18 0 17 Feb 2021
Federated Bandit: A Gossiping Approach Zhaowei Zhu Jingxuan Zhu Ji Liu Yang Liu FedML 139 83 0 24 Oct 2020
Reward-Free Exploration for Reinforcement Learning Chi Jin A. Krishnamurthy Max Simchowitz Tiancheng Yu OffRL 106 194 0 07 Feb 2020
MAVEN: Multi-Agent Variational Exploration Anuj Mahajan Tabish Rashid Mikayel Samvelyan Shimon Whiteson DRL 133 355 0 16 Oct 2019