Summary of Acceleration For Deep Reinforcement Learning Using Parallel and Distributed Computing: a Survey, by Zhihong Liu et al.
Acceleration for Deep Reinforcement Learning using Parallel and Distributed Computing: A Survey
by Zhihong Liu, Xin Xu, Peng Qiao, Dongsheng Li
First submitted to arxiv on: 8 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Distributed, Parallel, and Cluster Computing (cs.DC)
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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 This paper investigates the training acceleration methodologies for deep reinforcement learning using parallel and distributed computing. The authors provide a comprehensive survey of state-of-the-art methods and core references in this field. They also discuss emerging topics and open issues, including learning system architectures, simulation parallelism, computing parallelism, distributed synchronization mechanisms, and deep evolutionary reinforcement learning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In simple terms, this paper is about making it faster to train artificial intelligence models using many computers at the same time. This is important because training these models can take a very long time, even with powerful computers. The authors look at different ways to speed up the process and discuss what works best for each approach. They also compare 16 open-source libraries that people use to develop their own AI projects. |
Keywords
* Artificial intelligence * Reinforcement learning
