Prioritized League Reinforcement Learning for Large-Scale Heterogeneous Multiagent Systems
- URL: http://arxiv.org/abs/2403.18057v1
- Date: Tue, 26 Mar 2024 19:21:50 GMT
- Title: Prioritized League Reinforcement Learning for Large-Scale Heterogeneous Multiagent Systems
- Authors: Qingxu Fu, Zhiqiang Pu, Min Chen, Tenghai Qiu, Jianqiang Yi,
- Abstract summary: We propose a prioritized Heterogeneous League Reinforcement Learning (PHLRL) method to address large-scale heterogeneous cooperation problems.
We use Unreal Engine to design a large-scale heterogeneous cooperation benchmark named Large-Scale Multiagent Operation (LSMO)
- Score: 11.017749510087059
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Large-scale heterogeneous multiagent systems feature various realistic factors in the real world, such as agents with diverse abilities and overall system cost. In comparison to homogeneous systems, heterogeneous systems offer significant practical advantages. Nonetheless, they also present challenges for multiagent reinforcement learning, including addressing the non-stationary problem and managing an imbalanced number of agents with different types. We propose a Prioritized Heterogeneous League Reinforcement Learning (PHLRL) method to address large-scale heterogeneous cooperation problems. PHLRL maintains a record of various policies that agents have explored during their training and establishes a heterogeneous league consisting of diverse policies to aid in future policy optimization. Furthermore, we design a prioritized policy gradient approach to compensate for the gap caused by differences in the number of different types of agents. Next, we use Unreal Engine to design a large-scale heterogeneous cooperation benchmark named Large-Scale Multiagent Operation (LSMO), which is a complex two-team competition scenario that requires collaboration from both ground and airborne agents. We use experiments to show that PHLRL outperforms state-of-the-art methods, including QTRAN and QPLEX in LSMO.
Related papers
- An Extensible Framework for Open Heterogeneous Collaborative Perception [58.70875361688463]
Collaborative perception aims to mitigate the limitations of single-agent perception.
In this paper, we introduce a new open heterogeneous problem: how to accommodate continually emerging new heterogeneous agent types into collaborative perception.
We propose HEterogeneous ALliance (HEAL), a novel collaborative perception framework.
arXiv Detail & Related papers (2024-01-25T05:55:03Z) - Learning Heterogeneous Agent Cooperation via Multiagent League Training [6.801749815385998]
This work proposes a general-purpose reinforcement learning algorithm named Heterogeneous League Training (HLT) to address heterogeneous multiagent problems.
HLT keeps track of a pool of policies that agents have explored during training, gathering a league of heterogeneous policies to facilitate future policy optimization.
A hyper-network is introduced to increase the diversity of agent behaviors when collaborating with teammates having different levels of cooperation skills.
arXiv Detail & Related papers (2022-11-13T13:57:15Z) - A Policy Resonance Approach to Solve the Problem of Responsibility
Diffusion in Multiagent Reinforcement Learning [9.303181273699417]
Naively inheriting the single-agent exploration-exploitation strategy from single-agent algorithms causes potential collaboration failures.
We name this problem the Responsibility Diffusion (RD) as it shares similarities with a same-name social psychology effect.
We show that SOTA algorithms can equip this approach to promote the collaborative performance of agents in complex cooperative tasks.
arXiv Detail & Related papers (2022-08-16T13:56:00Z) - Policy Diagnosis via Measuring Role Diversity in Cooperative Multi-agent
RL [107.58821842920393]
We quantify the agent's behavior difference and build its relationship with the policy performance via bf Role Diversity
We find that the error bound in MARL can be decomposed into three parts that have a strong relation to the role diversity.
The decomposed factors can significantly impact policy optimization on three popular directions.
arXiv Detail & Related papers (2022-06-01T04:58:52Z) - Relative Distributed Formation and Obstacle Avoidance with Multi-agent
Reinforcement Learning [20.401609420707867]
We propose a distributed formation and obstacle avoidance method based on multi-agent reinforcement learning (MARL)
Our method achieves better performance regarding formation error, formation convergence rate and on-par success rate of obstacle avoidance compared with baselines.
arXiv Detail & Related papers (2021-11-14T13:02:45Z) - 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) - Efficient Model-Based Multi-Agent Mean-Field Reinforcement Learning [89.31889875864599]
We propose an efficient model-based reinforcement learning algorithm for learning in multi-agent systems.
Our main theoretical contributions are the first general regret bounds for model-based reinforcement learning for MFC.
We provide a practical parametrization of the core optimization problem.
arXiv Detail & Related papers (2021-07-08T18:01:02Z) - 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) - FACMAC: Factored Multi-Agent Centralised Policy Gradients [103.30380537282517]
We propose FACtored Multi-Agent Centralised policy gradients (FACMAC)
It is a new method for cooperative multi-agent reinforcement learning in both discrete and continuous action spaces.
We evaluate FACMAC on variants of the multi-agent particle environments, a novel multi-agent MuJoCo benchmark, and a challenging set of StarCraft II micromanagement tasks.
arXiv Detail & Related papers (2020-03-14T21:29:09Z) - Scalable Multi-Agent Inverse Reinforcement Learning via
Actor-Attention-Critic [54.2180984002807]
Multi-agent adversarial inverse reinforcement learning (MA-AIRL) is a recent approach that applies single-agent AIRL to multi-agent problems.
We propose a multi-agent inverse RL algorithm that is more sample-efficient and scalable than previous works.
arXiv Detail & Related papers (2020-02-24T20:30:45Z)
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.