HAVEN: Hierarchical Cooperative Multi-Agent Reinforcement Learning with Dual Coordination Mechanism

• URL: http://arxiv.org/abs/2110.07246v1
• Date: Thu, 14 Oct 2021 10:43:47 GMT
• Title: HAVEN: Hierarchical Cooperative Multi-Agent Reinforcement Learning with Dual Coordination Mechanism
• Authors: Zhiwei Xu, Yunpeng Bai, Bin Zhang, Dapeng Li, Guoliang Fan
• Abstract summary: 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.
• Score: 17.993973801986677
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multi-agent reinforcement learning often suffers from the exponentially larger action space caused by a large number of agents. In this paper, we propose a novel value decomposition framework HAVEN based on hierarchical reinforcement learning for the fully cooperative multi-agent problems. In order to address instabilities that arise from the concurrent optimization of high-level and low-level policies and another concurrent optimization of agents, we introduce the dual coordination mechanism of inter-layer strategies and inter-agent strategies. HAVEN does not require domain knowledge and pretraining at all, and can be applied to any value decomposition variants. Our method is demonstrated to achieve superior results to many baselines on StarCraft II micromanagement tasks and offers an efficient solution to multi-agent hierarchical reinforcement learning in fully cooperative scenarios.

Related papers

• Learning Emergence of Interaction Patterns across Independent RL Agents in Multi-Agent Environments [3.0284592792243794]
  Bottom Up Network (BUN) treats the collective of multi-agents as a unified entity. Our empirical evaluations across a variety of cooperative multi-agent scenarios, including tasks such as cooperative navigation and traffic control, consistently demonstrate BUN's superiority over baseline methods with substantially reduced computational costs.
  arXiv  Detail & Related papers  (2024-10-03T14:25:02Z)
• Effective Multi-Agent Deep Reinforcement Learning Control with Relative Entropy Regularization [6.441951360534903]
  Multi-Agent Continuous Dynamic Policy Gradient (MACDPP) was proposed to tackle the issues of limited capability and sample efficiency in various scenarios controlled by multiple agents. It alleviates the inconsistency of multiple agents' policy updates by introducing the relative entropy regularization to the Training with Decentralized Execution (CTDE) framework with the Actor-Critic (AC) structure.
  arXiv  Detail & Related papers  (2023-09-26T07:38:19Z)
• 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)
• A Variational Approach to Mutual Information-Based Coordination for Multi-Agent Reinforcement Learning [17.893310647034188]
  We propose a new mutual information framework for multi-agent reinforcement learning. Applying policy to maximize the derived lower bound, we propose a practical algorithm named variational maximum mutual information multi-agent actor-critic.
  arXiv  Detail & Related papers  (2023-03-01T12:21:30Z)
• Multi-agent Deep Covering Skill Discovery [50.812414209206054]
  We propose Multi-agent Deep Covering Option Discovery, which constructs the multi-agent options through minimizing the expected cover time of the multiple agents' joint state space. Also, we propose a novel framework to adopt the multi-agent options in the MARL process. We show that the proposed algorithm can effectively capture the agent interactions with the attention mechanism, successfully identify multi-agent options, and significantly outperforms prior works using single-agent options or no options.
  arXiv  Detail & Related papers  (2022-10-07T00:40:59Z)
• 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)
• Coach-assisted Multi-Agent Reinforcement Learning Framework for Unexpected Crashed Agents [120.91291581594773]
  We present a formal formulation of a cooperative multi-agent reinforcement learning system with unexpected crashes. We propose a coach-assisted multi-agent reinforcement learning framework, which introduces a virtual coach agent to adjust the crash rate during training. To the best of our knowledge, this work is the first to study the unexpected crashes in the multi-agent system.
  arXiv  Detail & Related papers  (2022-03-16T08:22:45Z)
• 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)
• 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.