Hierarchical Consensus-Based Multi-Agent Reinforcement Learning for Multi-Robot Cooperation Tasks

• URL: http://arxiv.org/abs/2407.08164v2
• Date: Fri, 23 Aug 2024 13:07:46 GMT
• Title: Hierarchical Consensus-Based Multi-Agent Reinforcement Learning for Multi-Robot Cooperation Tasks
• Authors: Pu Feng, Junkang Liang, Size Wang, Xin Yu, Xin Ji, Yiting Chen, Kui Zhang, Rongye Shi, Wenjun Wu,
• Abstract summary: We introduce the Hierarchical Consensus-based Multi-Agent Reinforcement Learning (HC-MARL) framework to address this limitation. HC-MARL employs contrastive learning to foster a global consensus among agents, enabling cooperative behavior without direct communication. To cater to the dynamic requirements of various tasks, consensus is divided into multiple layers, encompassing both short-term and long-term considerations.
• Score: 17.914928652949314
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In multi-agent reinforcement learning (MARL), the Centralized Training with Decentralized Execution (CTDE) framework is pivotal but struggles due to a gap: global state guidance in training versus reliance on local observations in execution, lacking global signals. Inspired by human societal consensus mechanisms, we introduce the Hierarchical Consensus-based Multi-Agent Reinforcement Learning (HC-MARL) framework to address this limitation. HC-MARL employs contrastive learning to foster a global consensus among agents, enabling cooperative behavior without direct communication. This approach enables agents to form a global consensus from local observations, using it as an additional piece of information to guide collaborative actions during execution. To cater to the dynamic requirements of various tasks, consensus is divided into multiple layers, encompassing both short-term and long-term considerations. Short-term observations prompt the creation of an immediate, low-layer consensus, while long-term observations contribute to the formation of a strategic, high-layer consensus. This process is further refined through an adaptive attention mechanism that dynamically adjusts the influence of each consensus layer. This mechanism optimizes the balance between immediate reactions and strategic planning, tailoring it to the specific demands of the task at hand. Extensive experiments and real-world applications in multi-robot systems showcase our framework's superior performance, marking significant advancements over baselines.

Related papers

• CaPo: Cooperative Plan Optimization for Efficient Embodied Multi-Agent Cooperation [98.11670473661587]
  CaPo improves cooperation efficiency with two phases: 1) meta-plan generation, and 2) progress-adaptive meta-plan and execution. Experimental results on the ThreeDworld Multi-Agent Transport and Communicative Watch-And-Help tasks demonstrate that CaPo achieves much higher task completion rate and efficiency compared with state-of-the-arts.
  arXiv  Detail & Related papers  (2024-11-07T13:08:04Z)
• Reaching Consensus in Cooperative Multi-Agent Reinforcement Learning with Goal Imagination [16.74629849552254]
  We propose a model-based consensus mechanism to explicitly coordinate multiple agents. The proposed Multi-agent Goal Imagination (MAGI) framework guides agents to reach consensus with an Imagined common goal. We show that such efficient consensus mechanism can guide all agents cooperatively reaching valuable future states.
  arXiv  Detail & Related papers  (2024-03-05T18:07:34Z)
• 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)
• 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)
• Hierarchical Reinforcement Learning with Opponent Modeling for Distributed Multi-agent Cooperation [13.670618752160594]
  Deep reinforcement learning (DRL) provides a promising approach for multi-agent cooperation through the interaction of the agents and environments. Traditional DRL solutions suffer from the high dimensions of multiple agents with continuous action space during policy search. We propose a hierarchical reinforcement learning approach with high-level decision-making and low-level individual control for efficient policy search.
  arXiv  Detail & Related papers  (2022-06-25T19:09:29Z)
• 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)
• Locality Matters: A Scalable Value Decomposition Approach for Cooperative Multi-Agent Reinforcement Learning [52.7873574425376]
  Cooperative multi-agent reinforcement learning (MARL) faces significant scalability issues due to state and action spaces that are exponentially large in the number of agents. We propose a novel, value-based multi-agent algorithm called LOMAQ, which incorporates local rewards in the Training Decentralized Execution paradigm.
  arXiv  Detail & Related papers  (2021-09-22T10:08:15Z)
• Cooperative Policy Learning with Pre-trained Heterogeneous Observation Representations [51.8796674904734]
  We propose a new cooperative learning framework with pre-trained heterogeneous observation representations. We employ an encoder-decoder based graph attention to learn the intricate interactions and heterogeneous representations.
  arXiv  Detail & Related papers  (2020-12-24T04:52:29Z)
• 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)
• F2A2: Flexible Fully-decentralized Approximate Actor-critic for Cooperative Multi-agent Reinforcement Learning [110.35516334788687]
  Decentralized multi-agent reinforcement learning algorithms are sometimes unpractical in complicated applications. We propose a flexible fully decentralized actor-critic MARL framework, which can handle large-scale general cooperative multi-agent setting. Our framework can achieve scalability and stability for large-scale environment and reduce information transmission.
  arXiv  Detail & Related papers  (2020-04-17T14:56:29Z)

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.