As well as many web pages, I wrote a series of white papers for TSG Consulting on the testing of both artificial intelligence (AI) and machine learning (ML). This is an extract from the fifth of these, which focused on reinforcement learning.
[Copy extract]
Reinforcement learning offers immense potential increases in efficiency across manufacturing and supply chains, as well as environmental benefits such as decreasing carbon emissions. Deep reinforcement learning in particular is producing results that consistently outperform supervised and unsupervised learning systems.
The response to the COVID pandemic has demonstrated what can be done in a short space of time, from mapping to projections, as well as the development and production of vaccines. All the same, these advances have been achieved by a combination of unsupervised, supervised and reinforcement learning approaches.
Other machine learning approaches will continue to be used, and to improve. Reduced training time is one key area where reinforcement learning will continue to defer to supervised learning, while the need for less data input ensures that unsupervised learning will also be preferred for certain applications. In addition, many applications simply don’t require the advanced levels of sophistication associated with reinforcement learning.
All the same, to make a machine creative, in the sense of seeking new ways to perform its tasks and meet goals, reinforcement learning is essentially the way ahead. All major tech companies have invested heavily in reinforcement learning research, while new companies are springing up to offer innovative products and to broaden potential applications. So while reinforcement learning isn’t the whole future of machine learning, it’s set to be the major part of it.
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