Summary of Knowledge Sharing and Transfer Via Centralized Reward Agent For Multi-task Reinforcement Learning, by Haozhe Ma et al.
Knowledge Sharing and Transfer via Centralized Reward Agent for Multi-Task Reinforcement Learning
by Haozhe Ma, Zhengding Luo, Thanh Vinh Vo, Kuankuan Sima, Tze-Yun Leong
First submitted to arxiv on: 20 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 A novel multi-task reinforcement learning framework is proposed, which integrates a centralized reward agent (CRA) and multiple distributed policy agents. This framework uses reward shaping to provide immediate feedback through auxiliary informative rewards, addressing the sparse-reward challenge in reinforcement learning. The CRA functions as a knowledge pool, distilling knowledge from various tasks and distributing it to individual policy agents to improve learning efficiency. Shaped rewards serve as a straightforward metric to encode knowledge, enhancing knowledge sharing across established tasks and adapting to new tasks by transferring valuable reward signals. The framework is validated on both discrete and continuous domains, demonstrating its robustness in multi-task sparse-reward settings and effective transferability to unseen tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers created a new way for machines to learn from multiple tasks at once. They did this by using a “reward agent” that helps other agents learn faster. This reward agent gives instant feedback, which is helpful when there’s not much information available (like in some game-like scenarios). The approach was tested on different types of problems and showed that it can help machines adapt to new tasks quickly. |
Keywords
» Artificial intelligence » Multi task » Reinforcement learning » Transferability
