The new method called cluster training with reinforcement (CRL) offers a more reasonable way. Instead of evaluating each situation separately, it combines similar situations in groups – clusters. This helps AI to find patterns and make decisions based on already accumulated experience.
The algorithm analyzes two basic indicators: how often the cluster is visited and how successful the actions are. Additional awards encourage AI to explore new and promising areas and not just randomly.
This technique gave high results in the tests, including the tasks of managing robots and the Atari game. In many respects, CRL has skipped modern algorithms. In this case, the method is easily integrated into the operating AI systems and does not require complete change.
With this approach, you can get a faster and safer learning, especially for autonomous transportation, energy and other responsible areas. CRL helps machines better adapt to reality, reduces the risk of error and reduces dependence on a person.
Source: Ferra
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