mlsanityData to 7 Oct 2026

K-nearest Multi-agent Deep Reinforcement Learning for Collaborative Tasks with a Variable Number of Agents

arXiv 2201.07092 ↗First posted 18 Jan 2022cs.LG cs.AI cs.MAcited by 2

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Abstract (arXiv)

Traditionally, the performance of multi-agent deep reinforcement learning algorithms are demonstrated and validated in gaming environments where we often have a fixed number of agents. In many industrial applications, the number of available agents can change at any given day and even when the number of agents is known ahead of time, it is common for an agent to break during the operation and become unavailable for a period of time. In this paper, we propose a new deep reinforcement learning algorithm for multi-agent collaborative tasks with a variable number of agents. We demonstrate the application of our algorithm using a fleet management simulator developed by Hitachi to generate realistic scenarios in a production site.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 9 of its 18 references that are in the map. First posted on arXiv 18 Jan 2022.

Builds on

9 of 18 references.

References · 9

  1. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,635
  2. 2017Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environmentscited by 1,519
  3. 2019Dota 2 with Large Scale Deep Reinforcement Learningcited by 1,084
  4. 2018QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learningcited by 697
  5. 2016Learning to Communicate with Deep Multi-Agent Reinforcement Learningcited by 561
  6. 2019Challenges of Real-World Reinforcement Learningcited by 282
  7. 2020An empirical investigation of the challenges of real-world reinforcement learningcited by 75
  8. 2020Dynamic Dispatching for Large-Scale Heterogeneous Fleet via Multi-agent Deep Reinforcement Learningcited by 4
  9. 2020Challenges of Applying Deep Reinforcement Learning in Dynamic Dispatchingcited by 4

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Citing papers per year: 2023: 1; 2024: 0; 2025: 1.

2 papers.

  1. 2023Multicopy Reinforcement Learning Agentscited by 0
  2. 2025Onboard Mission Replanning for Adaptive Cooperative Multi-Robot Systemscited by 0