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[2203.12759] Asynchronous Reinforcement Learning for Real-Time Control of Physical Robots
Source: ar5iv.labs.arxiv.org
Article summary:
1. This article presents a comparison between sequential and asynchronous reinforcement learning for real-time control of physical robots.
2. Experiments show that when the time cost of learning updates increases, the action cycle time in sequential implementation could grow excessively long, while the asynchronous implementation can always maintain an appropriate action cycle time.
3. The system learns in real-time to reach and track visual targets from pixels within two hours of experience and does so directly using real robots, learning completely from scratch.
Article analysis:
The article is generally reliable and trustworthy as it provides a systematic comparison between sequential and asynchronous reinforcement learning for real-time control of physical robots. The experiments conducted are well designed to test the effectiveness of both approaches under different conditions such as action cycle times, sensory data dimensions, and mini-batch sizes. Furthermore, the authors provide evidence for their claims by demonstrating that their system can learn in real-time to reach and track visual targets from pixels within two hours of experience using real robots.
However, there are some potential biases in the article which should be noted. For example, the authors only compare two approaches (sequential vs asynchronous) without exploring other possible solutions or counterarguments which may lead to a one-sided reporting of results. Additionally, there is no discussion on potential risks associated with either approach which could be beneficial for readers to consider before implementing either approach in their own projects. Finally, although the authors provide code for their system on Github, they do not provide any detailed explanation or instructions on how to use it which could be useful for readers who wish to replicate their results or build upon them.