Aligning Individual and Collective Objectives in Multi-Agent Cooperation

• URL: http://arxiv.org/abs/2402.12416v3
• Date: Tue, 22 Oct 2024 18:10:01 GMT
• Title: Aligning Individual and Collective Objectives in Multi-Agent Cooperation
• Authors: Yang Li, Wenhao Zhang, Jianhong Wang, Shao Zhang, Yali Du, Ying Wen, Wei Pan,
• Abstract summary: Mixed-motive cooperation is one of the most prominent challenges in multi-agent learning. We introduce a novel optimization method named textbftextitAltruistic textbftextitGradient textbftextitAdjustment (textbftextitAgA) that employs gradient adjustments to progressively align individual and collective objectives. We evaluate the effectiveness of our algorithm AgA through benchmark environments for testing mixed-motive collaboration with small-scale agents.
• Score: 18.082268221987956
• License:
• Abstract: Among the research topics in multi-agent learning, mixed-motive cooperation is one of the most prominent challenges, primarily due to the mismatch between individual and collective goals. The cutting-edge research is focused on incorporating domain knowledge into rewards and introducing additional mechanisms to incentivize cooperation. However, these approaches often face shortcomings such as the effort on manual design and the absence of theoretical groundings. To close this gap, we model the mixed-motive game as a differentiable game for the ease of illuminating the learning dynamics towards cooperation. More detailed, we introduce a novel optimization method named \textbf{\textit{A}}ltruistic \textbf{\textit{G}}radient \textbf{\textit{A}}djustment (\textbf{\textit{AgA}}) that employs gradient adjustments to progressively align individual and collective objectives. Furthermore, we theoretically prove that AgA effectively attracts gradients to stable fixed points of the collective objective while considering individual interests, and we validate these claims with empirical evidence. We evaluate the effectiveness of our algorithm AgA through benchmark environments for testing mixed-motive collaboration with small-scale agents such as the two-player public good game and the sequential social dilemma games, Cleanup and Harvest, as well as our self-developed large-scale environment in the game StarCraft II.

Related papers

• Efficient Adaptation in Mixed-Motive Environments via Hierarchical Opponent Modeling and Planning [51.52387511006586]
  We propose Hierarchical Opponent modeling and Planning (HOP), a novel multi-agent decision-making algorithm. HOP is hierarchically composed of two modules: an opponent modeling module that infers others' goals and learns corresponding goal-conditioned policies. HOP exhibits superior few-shot adaptation capabilities when interacting with various unseen agents, and excels in self-play scenarios.
  arXiv  Detail & Related papers  (2024-06-12T08:48:06Z)
• Large Language Model-based Human-Agent Collaboration for Complex Task Solving [94.3914058341565]
  We introduce the problem of Large Language Models (LLMs)-based human-agent collaboration for complex task-solving. We propose a Reinforcement Learning-based Human-Agent Collaboration method, ReHAC. This approach includes a policy model designed to determine the most opportune stages for human intervention within the task-solving process.
  arXiv  Detail & Related papers  (2024-02-20T11:03:36Z)
• Joint Intrinsic Motivation for Coordinated Exploration in Multi-Agent Deep Reinforcement Learning [0.0]
  We propose an approach for rewarding strategies where agents collectively exhibit novel behaviors. Jim rewards joint trajectories based on a centralized measure of novelty designed to function in continuous environments. Results show that joint exploration is crucial for solving tasks where the optimal strategy requires a high level of coordination.
  arXiv  Detail & Related papers  (2024-02-06T13:02:00Z)
• Reducing Optimism Bias in Incomplete Cooperative Games [0.0]
  We present a framework aimed at optimizing the sequence for revealing coalition values in cooperative games. Our contributions are threefold: (i) we study the individual players' optimistic completions of games with missing coalition values along with the arising gap, and investigate its analytical characteristics that facilitate more efficient optimization; (ii) we develop methods to minimize this gap over classes of games with a known prior by disclosing values of additional coalitions in both offline and online fashion; and (iii) we empirically demonstrate the algorithms' performance in practical scenarios.
  arXiv  Detail & Related papers  (2024-02-02T21:58:26Z)
• Cooperation Dynamics in Multi-Agent Systems: Exploring Game-Theoretic Scenarios with Mean-Field Equilibria [0.0]
  This paper investigates strategies to invoke cooperation in game-theoretic scenarios, namely the Iterated Prisoner's Dilemma. Existing cooperative strategies are analyzed for their effectiveness in promoting group-oriented behavior in repeated games. The study extends to scenarios with exponentially growing agent populations.
  arXiv  Detail & Related papers  (2023-09-28T08:57:01Z)
• Tackling Cooperative Incompatibility for Zero-Shot Human-AI Coordination [36.33334853998621]
  We introduce the Cooperative Open-ended LEarning (COLE) framework to solve cooperative incompatibility in learning. COLE formulates open-ended objectives in cooperative games with two players using perspectives of graph theory to evaluate and pinpoint the cooperative capacity of each strategy. We show that COLE could effectively overcome the cooperative incompatibility from theoretical and empirical analysis.
  arXiv  Detail & Related papers  (2023-06-05T16:51:38Z)
• Any-Play: An Intrinsic Augmentation for Zero-Shot Coordination [0.4153433779716327]
  We formalize an alternative criteria for evaluating cooperative AI, referred to as inter-algorithm cross-play. We show that existing state-of-the-art cooperative AI algorithms, such as Other-Play and Off-Belief Learning, under-perform in this paradigm. We propose the Any-Play learning augmentation for generalizing self-play-based algorithms to the inter-algorithm cross-play setting.
  arXiv  Detail & Related papers  (2022-01-28T21:43:58Z)
• ACP++: Action Co-occurrence Priors for Human-Object Interaction Detection [102.9428507180728]
  A common problem in the task of human-object interaction (HOI) detection is that numerous HOI classes have only a small number of labeled examples. We observe that there exist natural correlations and anti-correlations among human-object interactions. We present techniques to learn these priors and leverage them for more effective training, especially on rare classes.
  arXiv  Detail & Related papers  (2021-09-09T06:02:50Z)
• Cooperative Exploration for Multi-Agent Deep Reinforcement Learning [127.4746863307944]
  We propose cooperative multi-agent exploration (CMAE) for deep reinforcement learning. The goal is selected from multiple projected state spaces via a normalized entropy-based technique. We demonstrate that CMAE consistently outperforms baselines on various tasks.
  arXiv  Detail & Related papers  (2021-07-23T20:06:32Z)
• 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)
• Detecting Human-Object Interactions with Action Co-occurrence Priors [108.31956827512376]
  A common problem in human-object interaction (HOI) detection task is that numerous HOI classes have only a small number of labeled examples. We observe that there exist natural correlations and anti-correlations among human-object interactions. We present techniques to learn these priors and leverage them for more effective training, especially in rare classes.
  arXiv  Detail & Related papers  (2020-07-17T02:47:45Z)

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.