Summary of Multi-agent Continuous Control with Generative Flow Networks, by Shuang Luo et al.
Multi-Agent Continuous Control with Generative Flow Networks
by Shuang Luo, Yinchuan Li, Shunyu Liu, Xu Zhang, Yunfeng Shao, Chao Wu
First submitted to arxiv on: 13 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Multiagent Systems (cs.MA)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The Generative Flow Networks (GFlowNets) are designed to generate diverse trajectories from a distribution where the final states of the trajectories are proportional to the reward, serving as an 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. To address this limitation, we propose the Multi-Agent generative Continuous Flow Networks (MACFN) method that enables multiple agents to perform cooperative exploration for various compositional continuous objects. MACFN trains decentralized individual-flow-based policies in a centralized global-flow-based matching fashion, allowing agents to deliver actions solely based on their assigned local flow in a decentralized way, forming a joint policy distribution proportional to the rewards. Our proposed method outperforms state-of-the-art counterparts and demonstrates better exploration capability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary MACFN is a new approach that helps multiple robots work together to explore and learn about their environment. The goal is to create diverse paths for the robots to follow, depending on how well they do. Right now, this only works for single robots, but MACFN can help teams of robots work together too! By breaking down complex tasks into smaller parts and letting each robot focus on its own part, we can make it easier for multiple robots to work together effectively. |
Keywords
» Artificial intelligence » Reinforcement learning
