Cooperative and Stochastic Multi-Player Multi-Armed Bandit: Optimal Regret With Neither Communication Nor Collisions
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Abstract (arXiv)
We consider the cooperative multi-player version of the stochastic multi-armed bandit problem. We study the regime where the players cannot communicate but have access to shared randomness. In prior work by the first two authors, a strategy for this regime was constructed for two players and three arms, with regret tildeO(√T), and with no collisions at all between the players (with very high probability). In this paper we show that these properties (near-optimal regret and no collisions at all) are achievable for any number of players and arms. At a high level, the previous strategy heavily relied on a 2-dimensional geometric intuition that was difficult to generalize in higher dimensions, while here we take a more combinatorial route to build the new strategy.
Key facts. Cited by 13 papers in the map (data through 14 Oct 2026). First posted on arXiv 8 Nov 2020.
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Citing papers per year: 2021: 7; 2022: 4; 2023: 1; 2024: 1.
13 papers.
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- 2022A survey on multi-player banditscited by 19
- 2021Heterogeneous Multi-player Multi-armed Bandits: Closing the Gap and Generalizationcited by 18
- 2021Decentralized Cooperative Reinforcement Learning with Hierarchical Information Structurecited by 14
- 2021Collaborative Pure Exploration in Kernel Banditcited by 13
- 2021Towards Optimal Algorithms for Multi-Player Bandits without Collision Sensing Informationcited by 11
- 2022Decentralized, Communication- and Coordination-free Learning in Structured Matching Marketscited by 11
- 2021Decentralized Learning in Online Queuing Systemscited by 8
- 2021Multi-player Multi-armed Bandits with Collision-Dependent Reward Distributionscited by 8
- 2024Improved Bandits in Many-to-one Matching Markets with Incentive Compatibilitycited by 7
- 2021An Instance-Dependent Analysis for the Cooperative Multi-Player Multi-Armed Banditcited by 6
- 2022Cooperative Online Learning in Stochastic and Adversarial MDPscited by 4
- 2022The Pareto Frontier of Instance-Dependent Guarantees in Multi-Player Multi-Armed Bandits with no Communicationcited by 2
1 more
- 2024Improved Bandits in Many-to-one Matching Markets with Incentive Compatibility3 Jan 2024
- 2023Decentralized Online Bandit Optimization on Directed Graphs with Regret Bounds27 Jan 2023
- 2022A survey on multi-player bandits29 Nov 2022
- 2022Decentralized, Communication- and Coordination-free Learning in Structured Matching Markets6 Jun 2022
- 2022The Pareto Frontier of Instance-Dependent Guarantees in Multi-Player Multi-Armed Bandits with no Communication19 Feb 2022
- 2022Cooperative Online Learning in Stochastic and Adversarial MDPs31 Jan 2022
- 2021An Instance-Dependent Analysis for the Cooperative Multi-Player Multi-Armed Bandit8 Nov 2021
- 2021Decentralized Cooperative Reinforcement Learning with Hierarchical Information Structure1 Nov 2021
- 2021Collaborative Pure Exploration in Kernel Bandit29 Oct 2021
- 2021Heterogeneous Multi-player Multi-armed Bandits: Closing the Gap and Generalization27 Oct 2021
- 2021Multi-player Multi-armed Bandits with Collision-Dependent Reward Distributions25 Jun 2021
- 2021Decentralized Learning in Online Queuing Systems8 Jun 2021
- 2022A survey on multi-player banditscited by 19
- 2021Heterogeneous Multi-player Multi-armed Bandits: Closing the Gap and Generalizationcited by 18
- 2021Decentralized Cooperative Reinforcement Learning with Hierarchical Information Structurecited by 14
- 2021Collaborative Pure Exploration in Kernel Banditcited by 13
- 2021Towards Optimal Algorithms for Multi-Player Bandits without Collision Sensing Informationcited by 11
- 2022Decentralized, Communication- and Coordination-free Learning in Structured Matching Marketscited by 11
- 2021Decentralized Learning in Online Queuing Systemscited by 8
- 2021Multi-player Multi-armed Bandits with Collision-Dependent Reward Distributionscited by 8
- 2024Improved Bandits in Many-to-one Matching Markets with Incentive Compatibilitycited by 7
- 2021An Instance-Dependent Analysis for the Cooperative Multi-Player Multi-Armed Banditcited by 6
- 2022Cooperative Online Learning in Stochastic and Adversarial MDPscited by 4
- 2022The Pareto Frontier of Instance-Dependent Guarantees in Multi-Player Multi-Armed Bandits with no Communicationcited by 2
12 most cited of 13 All 13 (JSON)
- 2024Improved Bandits in Many-to-one Matching Markets with Incentive Compatibility3 Jan 2024
- 2023Decentralized Online Bandit Optimization on Directed Graphs with Regret Bounds27 Jan 2023
- 2022A survey on multi-player bandits29 Nov 2022
- 2022Decentralized, Communication- and Coordination-free Learning in Structured Matching Markets6 Jun 2022
- 2022The Pareto Frontier of Instance-Dependent Guarantees in Multi-Player Multi-Armed Bandits with no Communication19 Feb 2022
- 2022Cooperative Online Learning in Stochastic and Adversarial MDPs31 Jan 2022
- 2021An Instance-Dependent Analysis for the Cooperative Multi-Player Multi-Armed Bandit8 Nov 2021
- 2021Decentralized Cooperative Reinforcement Learning with Hierarchical Information Structure1 Nov 2021
- 2021Collaborative Pure Exploration in Kernel Bandit29 Oct 2021
- 2021Heterogeneous Multi-player Multi-armed Bandits: Closing the Gap and Generalization27 Oct 2021
- 2021Multi-player Multi-armed Bandits with Collision-Dependent Reward Distributions25 Jun 2021
- 2021Decentralized Learning in Online Queuing Systems8 Jun 2021
12 newest of 13 All 13 (JSON)