.png){kind=icon}
Return
Multi-agent Continuous Control with Generative Flow Networks
.png){kind=icon}
OA
AI
DOI：10.1016/j.neunet.2024.106243.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Generative Flow Networks (GFlowNets) aim to generate diverse trajectories from a distribution in which the final states of the trajectories are proportional to the reward, serving as a powerful alternative to reinforcement learning for exploratory control tasks. However, the individual -flow matching constraint in GFlowNets limits their applications for multi -agent systems, especially continuous joint -control problems. In this paper, we propose a novel Multi -Agent generative Continuous Flow Networks (MACFN) method to enable multiple agents to perform cooperative exploration for various compositional continuous objects. Technically, MACFN trains decentralized individual -flow -based policies in a centralized global -flow -based matching fashion. During centralized training, MACFN introduces a continuous flow decomposition network to deduce the flow contributions of each agent in the presence of only global rewards. Then agents can deliver actions solely based on their assigned local flow in a decentralized way, forming a joint policy distribution proportional to the rewards. To guarantee the expressiveness of continuous flow decomposition, we theoretically derive a consistency condition on the decomposition network. Experimental results demonstrate that the proposed method yields results superior to the state-of-the-art counterparts and better exploration capability. Our code is available at https://github.com/isluoshuang/MACFN.
Keywords:
Continuous Control
Generative Flow Networks
Multi-agent System
Journal
IF:
6.3
Papers:
8.3K
Citations:
3.0W
Organization
Cited Papers
IHG-MA: Inductive heterogeneous graph multi-agent reinforcement learning for multi-intersection traffic signal control.png)
NEURAL NETWORKS
NEURAL NETWORKSIF6.3
PREAI
ShareSaveHierarchical Attention Master-Slave for heterogeneous multi-agent reinforcement learningNEURAL NETWORKS
NEURAL NETWORKSIF6.3
PREAI
ShareSaveShareSaveShareSaveA leader-following paradigm based deep reinforcement learning method for multi-agent cooperation gamesNEURAL NETWORKS
NEURAL NETWORKSIF6.3
PREAI
ShareSave
View more