In this article, we explore a novel approach to facilitate collaborative reinforcement learning through communication-aided mixing. The proposed method, termed "CommMix," combines the strengths of both independent and centralized policies, enabling agents to learn and adapt in complex environments. By breaking policy parameters into smaller segments and segmenting them based on communication costs, CommMix allows for efficient mixture updates while ensuring each agent’s preferences are considered.
The proposed method is tested under various scenarios, demonstrating its effectiveness in improving average reward and reducing communication efficiency. The results show that CommMix outperforms existing methods, such as "Merge" and "Figure 8," in terms of both performance and efficiency. These findings suggest that CommMix offers a promising solution for facilitating collaborative reinforcement learning in complex environments.
Key Takeaways
- Introducing CommMix: A novel approach to facilitate collaborative reinforcement learning through communication-aided mixing.
- Breaking policy parameters into smaller segments and segmenting them based on communication costs enables efficient mixture updates while ensuring each agent’s preferences are considered.
- CommMix outperforms existing methods in terms of both performance and efficiency, demonstrating its effectiveness in complex environments.
