Summary of Hierarchical Consensus-based Multi-agent Reinforcement Learning For Multi-robot Cooperation Tasks, by Pu Feng et al.
Hierarchical Consensus-Based Multi-Agent Reinforcement Learning for Multi-Robot Cooperation Tasks
by Pu Feng, Junkang Liang, Size Wang, Xin Yu, Xin Ji, Yiting Chen, Kui Zhang, Rongye Shi, Wenjun Wu
First submitted to arxiv on: 11 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Multiagent Systems (cs.MA); Robotics (cs.RO)
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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 Hierarchical Consensus-based Multi-Agent Reinforcement Learning (HC-MARL) framework is introduced to address a limitation in Centralized Training with Decentralized Execution (CTDE) frameworks for multi-agent reinforcement learning. The limitation arises from the gap between global state guidance during training and reliance on local observations during execution, lacking global signals. 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. The framework consists of 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. An adaptive attention mechanism dynamically adjusts the influence of each consensus layer, optimizing the balance between immediate reactions and strategic planning. Extensive experiments and real-world applications in multi-robot systems demonstrate the framework’s superior performance, marking significant advancements over baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Multi-agent reinforcement learning is a way to get machines to work together to achieve a common goal. The problem with this approach is that it can be hard for the machines to agree on what to do when they don’t have all the information. To fix this, scientists created a new framework called Hierarchical Consensus-based Multi-Agent Reinforcement Learning (HC-MARL). This framework helps the machines come to an agreement by giving them hints about what to do based on their own observations and also considering the bigger picture. The HC-MARL framework has multiple layers that help the machines decide what to do in different situations, and it’s better at getting the machines to work together than other approaches. |
Keywords
» Artificial intelligence » Attention » Prompt » Reinforcement learning
