mlsanityData to 7 Oct 2026

Distributed Reinforcement Learning via Gossip

arXiv 1310.7610 ↗First posted 28 Oct 2013cs.DC cs.AI math.OCcited by 8

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Abstract (arXiv)

We consider the classical TD(0) algorithm implemented on a network of agents wherein the agents also incorporate the updates received from neighboring agents using a gossip-like mechanism. The combined scheme is shown to converge for both discounted and average cost problems.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). First posted on arXiv 28 Oct 2013.

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Citing papers per year: 2021: 2; 2022: 2; 2023: 1; 2024: 2; 2025: 1.

8 papers.

  1. 2021Adversarial attacks in consensus-based multi-agent reinforcement learningcited by 17
  2. 2024Acceleration for Deep Reinforcement Learning using Parallel and Distributed Computing: A Surveycited by 8
  3. 2021Multi-Agent Off-Policy TD Learning: Finite-Time Analysis with Near-Optimal Sample Complexity and Communication Complexitycited by 5
  4. 2022Reinforcement Learning with Unbiased Policy Evaluation and Linear Function Approximationcited by 4
  5. 2023A primal-dual perspective for distributed TD-learningcited by 1
  6. 2024A finite time analysis of distributed Q-learningcited by 1
  7. 2025Nonparametric Bellman Mappings for Value Iteration in Distributed Reinforcement Learningcited by 1
  8. 2022Exact Formulas for Finite-Time Estimation Errors of Decentralized Temporal Difference Learning with Linear Function Approximationcited by 0