Nucleolus Credit Assignment for Effective Coalitions in Multi-agent Reinforcement Learning

• URL: http://arxiv.org/abs/2503.00372v1
• Date: Sat, 01 Mar 2025 07:01:58 GMT
• Title: Nucleolus Credit Assignment for Effective Coalitions in Multi-agent Reinforcement Learning
• Authors: Yugu Li, Zehong Cao, Jianglin Qiao, Siyi Hu,
• Abstract summary: In cooperative multi-agent reinforcement learning (MARL), agents typically form a single grand coalition based on credit assignment to tackle a composite task.<n>This paper proposed a nucleolus-based credit assignment grounded in cooperative game theory, enabling the autonomous partitioning of agents into small coalitions.
• Score: 13.220552085613292
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In cooperative multi-agent reinforcement learning (MARL), agents typically form a single grand coalition based on credit assignment to tackle a composite task, often resulting in suboptimal performance. This paper proposed a nucleolus-based credit assignment grounded in cooperative game theory, enabling the autonomous partitioning of agents into multiple small coalitions that can effectively identify and complete subtasks within a larger composite task. Specifically, our designed nucleolus Q-learning could assign fair credits to each agent, and the nucleolus Q-operator provides theoretical guarantees with interpretability for both learning convergence and the stability of the formed small coalitions. Through experiments on Predator-Prey and StarCraft scenarios across varying difficulty levels, our approach demonstrated the emergence of multiple effective coalitions during MARL training, leading to faster learning and superior performance in terms of win rate and cumulative rewards especially in hard and super-hard environments, compared to four baseline methods. Our nucleolus-based credit assignment showed the promise for complex composite tasks requiring effective subteams of agents.

Related papers

• CORA: Coalitional Rational Advantage Decomposition for Multi-Agent Policy Gradients [1.1592964544667297]
  This work focuses on the credit assignment problem in cooperative multi-agent reinforcement learning (MARL)<n>We propose a credit assignment method called Coalitional Rational Advantage Decomposition (CORA)<n>Experiments on matrix games, differential games, and multi-agent collaboration benchmarks demonstrate that CORA outperforms strong baselines.
  arXiv  Detail & Related papers  (2025-06-03T08:04:43Z)
• Multi-Agent Collaboration via Evolving Orchestration [61.93162413517026]
  Large language models (LLMs) have achieved remarkable results across diverse downstream tasks, but their monolithic nature restricts scalability and efficiency in complex problem-solving.<n>We propose a puppeteer-style paradigm for LLM-based multi-agent collaboration, where a central orchestrator dynamically directs agents in response to evolving task states.<n> Experiments on closed- and open-domain scenarios show that this method achieves superior performance with reduced computational costs.
  arXiv  Detail & Related papers  (2025-05-26T07:02:17Z)
• Learning Reward Machines in Cooperative Multi-Agent Tasks [75.79805204646428]
  This paper presents a novel approach to Multi-Agent Reinforcement Learning (MARL) It combines cooperative task decomposition with the learning of reward machines (RMs) encoding the structure of the sub-tasks. The proposed method helps deal with the non-Markovian nature of the rewards in partially observable environments.
  arXiv  Detail & Related papers  (2023-03-24T15:12:28Z)
• Adaptive Value Decomposition with Greedy Marginal Contribution Computation for Cooperative Multi-Agent Reinforcement Learning [48.41925886860991]
  Real-world cooperation often requires intensive coordination among agents simultaneously. Traditional methods that learn the value function as a monotonic mixing of per-agent utilities cannot solve the tasks with non-monotonic returns. We propose a novel explicit credit assignment method to address the non-monotonic problem.
  arXiv  Detail & Related papers  (2023-02-14T07:23:59Z)
• RACA: Relation-Aware Credit Assignment for Ad-Hoc Cooperation in Multi-Agent Deep Reinforcement Learning [55.55009081609396]
  We propose a novel method, called Relation-Aware Credit Assignment (RACA), which achieves zero-shot generalization in ad-hoc cooperation scenarios. RACA takes advantage of a graph-based encoder relation to encode the topological structure between agents. Our method outperforms baseline methods on the StarCraftII micromanagement benchmark and ad-hoc cooperation scenarios.
  arXiv  Detail & Related papers  (2022-06-02T03:39:27Z)
• LDSA: Learning Dynamic Subtask Assignment in Cooperative Multi-Agent Reinforcement Learning [122.47938710284784]
  We propose a novel framework for learning dynamic subtask assignment (LDSA) in cooperative MARL. To reasonably assign agents to different subtasks, we propose an ability-based subtask selection strategy. We show that LDSA learns reasonable and effective subtask assignment for better collaboration.
  arXiv  Detail & Related papers  (2022-05-05T10:46:16Z)
• HAVEN: Hierarchical Cooperative Multi-Agent Reinforcement Learning with Dual Coordination Mechanism [17.993973801986677]
  Multi-agent reinforcement learning often suffers from the exponentially larger action space caused by a large number of agents. We propose a novel value decomposition framework HAVEN based on hierarchical reinforcement learning for the fully cooperative multi-agent problems.
  arXiv  Detail & Related papers  (2021-10-14T10:43:47Z)
• Celebrating Diversity in Shared Multi-Agent Reinforcement Learning [20.901606233349177]
  Deep multi-agent reinforcement learning has shown the promise to solve complex cooperative tasks. In this paper, we aim to introduce diversity in both optimization and representation of shared multi-agent reinforcement learning. Our method achieves state-of-the-art performance on Google Research Football and super hard StarCraft II micromanagement tasks.
  arXiv  Detail & Related papers  (2021-06-04T00:55:03Z)
• Shapley Counterfactual Credits for Multi-Agent Reinforcement Learning [34.856522993714535]
  We propose Shapley Counterfactual Credit Assignment, a novel method for explicit credit assignment which accounts for the coalition of agents. Our method outperforms existing cooperative MARL algorithms significantly and achieves the state-of-the-art, with especially large margins on tasks with more severe difficulties.
  arXiv  Detail & Related papers  (2021-06-01T07:38:34Z)
• Structured Diversification Emergence via Reinforced Organization Control and Hierarchical Consensus Learning [48.525944995851965]
  We propose a structured diversification emergence MARL framework named scRochico based on reinforced organization control and hierarchical consensus learning. scRochico is significantly better than the current SOTA algorithms in terms of exploration efficiency and cooperation strength.
  arXiv  Detail & Related papers  (2021-02-09T11:46:12Z)
• UneVEn: Universal Value Exploration for Multi-Agent Reinforcement Learning [53.73686229912562]
  We propose a novel MARL approach called Universal Value Exploration (UneVEn) UneVEn learns a set of related tasks simultaneously with a linear decomposition of universal successor features. Empirical results on a set of exploration games, challenging cooperative predator-prey tasks requiring significant coordination among agents, and StarCraft II micromanagement benchmarks show that UneVEn can solve tasks where other state-of-the-art MARL methods fail.
  arXiv  Detail & Related papers  (2020-10-06T19:08:47Z)
• Reward Machines for Cooperative Multi-Agent Reinforcement Learning [30.84689303706561]
  In cooperative multi-agent reinforcement learning, a collection of agents learns to interact in a shared environment to achieve a common goal. We propose the use of reward machines (RM) -- Mealy machines used as structured representations of reward functions -- to encode the team's task. The proposed novel interpretation of RMs in the multi-agent setting explicitly encodes required teammate interdependencies, allowing the team-level task to be decomposed into sub-tasks for individual agents.
  arXiv  Detail & Related papers  (2020-07-03T23:08:14Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.