Summary of Online Policy Distillation with Decision-attention, by Xinqiang Yu et al.
Online Policy Distillation with Decision-Attention
by Xinqiang Yu, Chuanguang Yang, Chengqing Yu, Libo Huang, Zhulin An, Yongjun Xu
First submitted to arxiv on: 8 Jun 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 Policy Distillation (PD) has been a successful approach to enhance deep reinforcement learning tasks. PD distills policy knowledge from a teacher agent to a student agent. However, the teacher-student framework relies on a well-trained teacher model, which can be computationally expensive. To address this limitation, we propose Online Policy Distillation (OPD) with Decision-Attention (DA), an online learning framework where multiple policies operate in the same environment, sharing knowledge and improving performance together. Without a well-performing teacher policy, group-derived targets play a crucial role in transferring group knowledge to each student policy. To mitigate the homogenization of student policies, we introduce the Decision-Attention module, generating distinct weights for each policy. Our method outperforms independent training on both PPO and DQN algorithms across various Atari tasks. This demonstrates the effectiveness of OPD-DA in transferring knowledge between different policies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you have a team of artificial agents trying to learn from experience. They need to work together to get better, but they each see things differently. That’s where Policy Distillation comes in – it helps them share what they’ve learned and improve together. The problem is that someone needs to teach them, but that can be hard and time-consuming. To fix this, we created a new way for the agents to learn from each other. We call it Online Policy Distillation with Decision-Attention. It lets the agents work together and share their knowledge in real-time. Our tests showed that this method helps the agents get better faster than if they were working alone. This means we can use this approach to make artificial intelligence systems more effective. |
Keywords
» Artificial intelligence » Attention » Distillation » Online learning » Reinforcement learning » Teacher model
