Summary of Distrl: An Asynchronous Distributed Reinforcement Learning Framework For On-device Control Agents, by Taiyi Wang et al.
DistRL: An Asynchronous Distributed Reinforcement Learning Framework for On-Device Control Agents
by Taiyi Wang, Zhihao Wu, Jianheng Liu, Jianye Hao, Jun Wang, Kun Shao
First submitted to arxiv on: 18 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Distributed, Parallel, and Cluster Computing (cs.DC); Systems and Control (eess.SY)
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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 framework called DistRL is introduced to enhance the efficiency of online reinforcement learning (RL) fine-tuning for mobile device control agents. By leveraging centralized training and decentralized data acquisition, DistRL ensures efficient fine-tuning in dynamic online interactions. The framework is backed by a tailor-made RL algorithm that balances exploration with data utilization for stable training. Experimental results show that DistRL achieves a 3X improvement in training efficiency and 2.4X faster data collection compared to leading synchronous methods. Additionally, trained agents using DistRL exhibit a 20% relative improvement in success rate on general Android tasks from an open benchmark. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary On-device control agents help users interact with mobile devices seamlessly. Integrating large language models makes these interactions more complex and efficient. However, training these models on devices is challenging due to limited data and inefficient processes. This paper introduces a new framework called DistRL that solves this problem by using centralized training and decentralized data collection. The framework also includes a special algorithm that balances exploring new things with using the data collected so far. The results show that DistRL is 3 times faster at training and collects data 2.4 times faster than other methods. Additionally, trained agents perform better by 20% compared to existing approaches. |
Keywords
» Artificial intelligence » Fine tuning » Reinforcement learning
