When Is Diversity Rewarded in Cooperative Multi-Agent Learning?

• URL: http://arxiv.org/abs/2506.09434v1
• Date: Wed, 11 Jun 2025 06:33:55 GMT
• Title: When Is Diversity Rewarded in Cooperative Multi-Agent Learning?
• Authors: Michael Amir, Matteo Bettini, Amanda Prorok,
• Abstract summary: We use multi-agent reinforcement learning (MARL) as our computational paradigm.<n>We introduce Heterogeneous Environment Design (HED), a gradient-based algorithm that optimize the parameter space of underspecified MARL environments.
• Score: 7.380976669029464
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The success of teams in robotics, nature, and society often depends on the division of labor among diverse specialists; however, a principled explanation for when such diversity surpasses a homogeneous team is still missing. Focusing on multi-agent task allocation problems, our goal is to study this question from the perspective of reward design: what kinds of objectives are best suited for heterogeneous teams? We first consider an instantaneous, non-spatial setting where the global reward is built by two generalized aggregation operators: an inner operator that maps the $N$ agents' effort allocations on individual tasks to a task score, and an outer operator that merges the $M$ task scores into the global team reward. We prove that the curvature of these operators determines whether heterogeneity can increase reward, and that for broad reward families this collapses to a simple convexity test. Next, we ask what incentivizes heterogeneity to emerge when embodied, time-extended agents must learn an effort allocation policy. To study heterogeneity in such settings, we use multi-agent reinforcement learning (MARL) as our computational paradigm, and introduce Heterogeneous Environment Design (HED), a gradient-based algorithm that optimizes the parameter space of underspecified MARL environments to find scenarios where heterogeneity is advantageous. Experiments in matrix games and an embodied Multi-Goal-Capture environment show that, despite the difference in settings, HED rediscovers the reward regimes predicted by our theory to maximize the advantage of heterogeneity, both validating HED and connecting our theoretical insights to reward design in MARL. Together, these results help us understand when behavioral diversity delivers a measurable benefit.

Related papers

• Learning Individual Intrinsic Reward in Multi-Agent Reinforcement Learning via Incorporating Generalized Human Expertise [6.441011477647557]
  Efficient exploration in multi-agent reinforcement learning (MARL) is a challenging problem when receiving only a team reward.<n>A powerful method to mitigate this issue involves crafting dense individual rewards to guide the agents toward efficient exploration.<n>We propose a novel framework, LIGHT, which can integrate human knowledge into MARL algorithms in an end-to-end manner.
  arXiv  Detail & Related papers  (2025-07-25T00:59:10Z)
• HyperMARL: Adaptive Hypernetworks for Multi-Agent RL [9.154125291830058]
  HyperMARL is a PS approach using hypernetworks for dynamic agent-specific parameters.<n>It reduces policy gradient variance, facilitates shared-policy adaptation, and helps mitigate cross-agent interference.<n>These findings establish HyperMARL as a versatile approach for adaptive MARL.
  arXiv  Detail & Related papers  (2024-12-05T15:09:51Z)
• Enhancing Heterogeneous Multi-Agent Cooperation in Decentralized MARL via GNN-driven Intrinsic Rewards [1.179778723980276]
  Multi-agent Reinforcement Learning (MARL) is emerging as a key framework for sequential decision-making and control tasks. The deployment of these systems in real-world scenarios often requires decentralized training, a diverse set of agents, and learning from infrequent environmental reward signals. We propose the CoHet algorithm, which utilizes a novel Graph Neural Network (GNN) based intrinsic motivation to facilitate the learning of heterogeneous agent policies.
  arXiv  Detail & Related papers  (2024-08-12T21:38:40Z)
• Task Groupings Regularization: Data-Free Meta-Learning with Heterogeneous Pre-trained Models [83.02797560769285]
  Data-Free Meta-Learning (DFML) aims to derive knowledge from a collection of pre-trained models without accessing their original data.<n>Current methods often overlook the heterogeneity among pre-trained models, which leads to performance degradation due to task conflicts.
  arXiv  Detail & Related papers  (2024-05-26T13:11:55Z)
• Collaborative Training of Heterogeneous Reinforcement Learning Agents in Environments with Sparse Rewards: What and When to Share? [7.489793155793319]
  This work focuses on combining information obtained through intrinsic motivation with the aim of having a more efficient exploration and faster learning. Our results reveal different ways in which a collaborative framework with little additional computational cost can outperform an independent learning process without knowledge sharing.
  arXiv  Detail & Related papers  (2022-02-24T16:15:51Z)
• Generalization in Cooperative Multi-Agent Systems [49.16349318581611]
  We study the theoretical underpinnings of Combinatorial Generalization (CG) for cooperative multi-agent systems. CG is a highly desirable trait for autonomous systems as it can increase their utility and deployability across a wide range of applications.
  arXiv  Detail & Related papers  (2022-01-31T21:39:56Z)
• Robust Allocations with Diversity Constraints [65.3799850959513]
  We show that the Nash Welfare rule that maximizes product of agent values is uniquely positioned to be robust when diversity constraints are introduced. We also show that the guarantees achieved by Nash Welfare are nearly optimal within a widely studied class of allocation rules.
  arXiv  Detail & Related papers  (2021-09-30T11:09:31Z)
• 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)
• Heterogeneous Explore-Exploit Strategies on Multi-Star Networks [0.0]
  We study a class of distributed bandit problems in which agents communicate over a multi-star network. We propose new heterogeneous explore-exploit strategies using the multi-star as the model irregular network graph.
  arXiv  Detail & Related papers  (2020-09-02T20:56:49Z)
• Randomized Entity-wise Factorization for Multi-Agent Reinforcement Learning [59.62721526353915]
  Multi-agent settings in the real world often involve tasks with varying types and quantities of agents and non-agent entities. Our method aims to leverage these commonalities by asking the question: What is the expected utility of each agent when only considering a randomly selected sub-group of its observed entities?''
  arXiv  Detail & Related papers  (2020-06-07T18:28:41Z)
• When Relation Networks meet GANs: Relation GANs with Triplet Loss [110.7572918636599]
  Training stability is still a lingering concern of generative adversarial networks (GANs) In this paper, we explore a relation network architecture for the discriminator and design a triplet loss which performs better generalization and stability. Experiments on benchmark datasets show that the proposed relation discriminator and new loss can provide significant improvement on variable vision tasks.
  arXiv  Detail & Related papers  (2020-02-24T11:35:28Z)

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.