mlsanityData to 7 Oct 2026

Promoting Cooperation in Multi-Agent Reinforcement Learning via Mutual Help

arXiv 2302.09277 ↗First posted 18 Feb 2023cs.LG cs.AI cs.MAcited by 0

Not on the map. Why?

Abstract (arXiv)

Multi-agent reinforcement learning (MARL) has achieved great progress in cooperative tasks in recent years. However, in the local reward scheme, where only local rewards for each agent are given without global rewards shared by all the agents, traditional MARL algorithms lack sufficient consideration of agents' mutual influence. In cooperative tasks, agents' mutual influence is especially important since agents are supposed to coordinate to achieve better performance. In this paper, we propose a novel algorithm Mutual-Help-based MARL (MH-MARL) to instruct agents to help each other in order to promote cooperation. MH-MARL utilizes an expected action module to generate expected other agents' actions for each particular agent. Then, the expected actions are delivered to other agents for selective imitation during training. Experimental results show that MH-MARL improves the performance of MARL both in success rate and cumulative reward.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 11 of its 21 references that are in the map. First posted on arXiv 18 Feb 2023.

Builds on

11 of 21 references.

References · 11

  1. 2017Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environmentscited by 1,519
  2. 2017Counterfactual Multi-Agent Policy Gradientscited by 733
  3. 2018QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learningcited by 697
  4. 2016Learning Multiagent Communication with Backpropagationcited by 355
  5. 2018Learning Attentional Communication for Multi-Agent Cooperationcited by 170
  6. 2020Shared Experience Actor-Critic for Multi-Agent Reinforcement Learningcited by 114
  7. 2021Multi-Agent Reinforcement Learning for Active Voltage Control on Power Distribution Networkscited by 52
  8. 2019Reducing Overestimation Bias in Multi-Agent Domains Using Double Centralized Criticscited by 46
  9. 2018Learning to Teach in Cooperative Multiagent Reinforcement Learningcited by 45
  10. 2021Action Advising with Advice Imitation in Deep Reinforcement Learningcited by 10
  11. 2020Multi-Agent Reinforcement Learning for Problems with Combined Individual and Team Rewardcited by 8

Built on by

Not cited yet.