mlsanityData to 14 Oct 2026

Effective Multi-Agent Deep Reinforcement Learning Control with Relative Entropy Regularization

arXiv 2309.14727 ↗First posted 26 Sep 2023eess.SY cs.AI cs.LGcited by 5

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Abstract (arXiv)

In this paper, a novel Multi-agent Reinforcement Learning (MARL) approach, Multi-Agent Continuous Dynamic Policy Gradient (MACDPP) was proposed to tackle the issues of limited capability and sample efficiency in various scenarios controlled by multiple agents. It alleviates the inconsistency of multiple agents' policy updates by introducing the relative entropy regularization to the Centralized Training with Decentralized Execution (CTDE) framework with the Actor-Critic (AC) structure. Evaluated by multi-agent cooperation and competition tasks and traditional control tasks including OpenAI benchmarks and robot arm manipulation, MACDPP demonstrates significant superiority in learning capability and sample efficiency compared with both related multi-agent and widely implemented signal-agent baselines and therefore expands the potential of MARL in effectively learning challenging control scenarios.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 18 of its 44 references that are in the map. First posted on arXiv 26 Sep 2023.

Builds on

18 of 44 references.

References · 18

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,885
  3. 2015Continuous control with deep reinforcement learningcited by 3,407
  4. 2018Addressing Function Approximation Error in Actor-Critic Methodscited by 2,417
  5. 2017Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environmentscited by 1,558
  6. 2021The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Gamescited by 942
  7. 2019The StarCraft Multi-Agent Challengecited by 597
  8. 2016Learning to Communicate with Deep Multi-Agent Reinforcement Learningcited by 575
  9. 2020Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learningcited by 485
  10. 2021How to Train Your Robot with Deep Reinforcement Learning; Lessons We've Learnedcited by 213
  11. 2015Multiagent Cooperation and Competition with Deep Reinforcement Learningcited by 184
  12. 2019A Review of Cooperative Multi-Agent Deep Reinforcement Learningcited by 147
6 more
  1. 2019Reducing Overestimation Bias in Multi-Agent Domains Using Double Centralized Criticscited by 47
  2. 2021Deep Multiagent Reinforcement Learning: Challenges and Directionscited by 42
  3. 2019SMIX(λ): Enhancing Centralized Value Functions for Cooperative Multi-Agent Reinforcement Learningcited by 15
  4. 2022Unified Automatic Control of Vehicular Systems with Reinforcement Learningcited by 13
  5. 2020robo-gym -- An Open Source Toolkit for Distributed Deep Reinforcement Learning on Real and Simulated Robotscited by 9
  6. 2022UNMAS: Multi-Agent Reinforcement Learning for Unshaped Cooperative Scenarioscited by 9

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 2; 2025: 1; 2026: 2.

5 papers.

  1. 2024Towards Fault Tolerance in Multi-Agent Reinforcement Learningcited by 1
  2. 2025Effective Reinforcement Learning Control using Conservative Soft Actor-Criticcited by 1
  3. 2026Entropy-Regularized Reinforcement Learning for Linear-Quadratic Stackelberg Differential Games in Regime-Switching Diffusion Modelscited by 1
  4. 2024Edge Delayed Deep Deterministic Policy Gradient: efficient continuous control for edge scenarioscited by 0
  5. 2026Efficient Real-World Online Reinforcement Learning for Robot Manipulation via Centralized Training and Critic Decompositioncited by 0