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Coordinated Exploration in Concurrent Reinforcement Learning

arXiv 1802.01282 ↗First posted 5 Feb 2018cs.AIcited by 17

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Abstract (arXiv)

We consider a team of reinforcement learning agents that concurrently learn to operate in a common environment. We identify three properties - adaptivity, commitment, and diversity - which are necessary for efficient coordinated exploration and demonstrate that straightforward extensions to single-agent optimistic and posterior sampling approaches fail to satisfy them. As an alternative, we propose seed sampling, which extends posterior sampling in a manner that meets these requirements. Simulation results investigate how per-agent regret decreases as the number of agents grows, establishing substantial advantages of seed sampling over alternative exploration schemes.

Key facts. Cited by 17 papers in the map (data through 7 Oct 2026). First posted on arXiv 5 Feb 2018.

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Citing papers per year: 2021: 4; 2022: 2; 2023: 5; 2024: 2; 2025: 2; 2026: 2.

17 papers.

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  1. 2021Cooperative Exploration for Multi-Agent Deep Reinforcement Learningcited by 57
  2. 2021Exploration in Deep Reinforcement Learning: From Single-Agent to Multiagent Domaincited by 57
  3. 2022An Analysis of Ensemble Samplingcited by 23
  4. 2021Provably Efficient Multi-Task Reinforcement Learning with Model Transfercited by 16
  5. 2021No-Regret Reinforcement Learning with Heavy-Tailed Rewardscited by 13
  6. 2023Social Contract AI: Aligning AI Assistants with Implicit Group Normscited by 9
  7. 2023Ensemble sampling for linear bandits: small ensembles sufficecited by 7
  8. 2023Bayesian Reinforcement Learning with Limited Cognitive Loadcited by 4
  9. 2025Enhancing Diversity in Parallel Agents: A Maximum State Entropy Exploration Storycited by 4
  10. 2022One Policy is Enough: Parallel Exploration with a Single Policy is Near-Optimal for Reward-Free Reinforcement Learningcited by 2
  11. 2023Dual Self-Awareness Value Decomposition Framework without Individual Global Max for Cooperative Multi-Agent Reinforcement Learningcited by 1
  12. 2024Umbrella Reinforcement Learning -- computationally efficient tool for hard non-linear problemscited by 1
5 more
  1. 2023Adaptive Interventions with User-Defined Goals for Health Behavior Changecited by 0
  2. 2024Causal Coordinated Concurrent Reinforcement Learningcited by 0
  3. 2025Concurrent Learning with Aggregated States via Randomized Least Squares Value Iterationcited by 0
  4. 2026K-Myriad: Jump-starting reinforcement learning with unsupervised parallel agentscited by 0
  5. 2026The Horizon Threshold in Cooperative Multi-Agent Reward-Free Explorationcited by 0