mlsanityData to 14 Oct 2026

Optimization for Reinforcement Learning: From Single Agent to Cooperative Agents

arXiv 1912.00498 ↗First posted 1 Dec 2019cs.LG cs.AI cs.MAcited by 20

Not on the map. Why?

Abstract (arXiv)

This article reviews recent advances in multi-agent reinforcement learning algorithms for large-scale control systems and communication networks, which learn to communicate and cooperate. We provide an overview of this emerging field, with an emphasis on the decentralized setting under different coordination protocols. We highlight the evolution of reinforcement learning algorithms from single-agent to multi-agent systems, from a distributed optimization perspective, and conclude with future directions and challenges, in the hope to catalyze the growing synergy among distributed optimization, signal processing, and reinforcement learning communities.

Key facts. Cited by 20 papers in the map (data through 14 Oct 2026). First posted on arXiv 1 Dec 2019.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 10; 2022: 1; 2023: 1; 2024: 3; 2025: 3; 2026: 2.

20 papers.

Sort:

By area:

  1. 2021Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applicationscited by 97
  2. 2021Deep Reinforcement Learning Versus Evolution Strategies: A Comparative Surveycited by 28
  3. 2021Pervasive AI for IoT applications: A Survey on Resource-efficient Distributed Artificial Intelligencecited by 18
  4. 2025A Survey on Agent Workflow -- Status and Futurecited by 14
  5. 2021Learning to Coordinate in Multi-Agent Systems: A Coordinated Actor-Critic Algorithm and Finite-Time Guaranteescited by 6
  6. 2021MARL with General Utilities via Decentralized Shadow Reward Actor-Criticcited by 5
  7. 2021Kolmogorov-Smirnov Test-Based Actively-Adaptive Thompson Sampling for Non-Stationary Banditscited by 4
  8. 2021Towards Learning Generalizable Driving Policies from Restricted Latent Representationscited by 4
  9. 2021Reinforcement Learning for Intelligent Healthcare Systems: A Comprehensive Surveycited by 3
  10. 2025Multilinear Tensor Low-Rank Approximation for Policy-Gradient Methods in Reinforcement Learningcited by 3
  11. 2024Matrix Low-Rank Approximation For Policy Gradient Methodscited by 2
  12. 2024FlowRL: Flow-Augmented Few-Shot Reinforcement Learning for Semi-Structured Sensor Datacited by 2
8 more
  1. 2024Reinforcement Learning for Bidding Strategy Optimization in Day-Ahead Energy Marketcited by 2
  2. 2021Convergence Rates of Average-Reward Multi-agent Reinforcement Learning via Randomized Linear Programmingcited by 1
  3. 2021Distributed Policy Gradient with Variance Reduction in Multi-Agent Reinforcement Learningcited by 1
  4. 2025Feature-Aware Task-to-Core Allocation in Embedded Multi-core Platforms via Statistical Learningcited by 1
  5. 2022Cooperative Actor-Critic via TD Error Aggregationcited by 0
  6. 2023Continuous-Time Distributed Dynamic Programming for Networked Multi-Agent Markov Decision Processescited by 0
  7. 2026Offline Multi-Agent Reinforcement Learning for 6G Communications: Fundamentals, Applications and Future Directionscited by 0
  8. 2026ZeroDVFS: Zero-Shot LLM-Guided Core and Frequency Allocation for Embedded Platformscited by 0