Evolution Strategies for Reinforcement Learning

In the last article, the goal that we set to ourselves was to optimize the Deep Q-Learning with prioritized experience replay, in other words, provide the algorithm with a bit of help judging what is important and should be remembered and what is not. Most globally, under current technological achievements, algorithms tend to perform better when helped by human intervention. Take the example of image recognition, and let's say you want to classify apples and bananas. Your algorithm would definitely be more accurate with the prior knowledge that bananas are yellow than if it has to learn by itself. This could also be translated by over engineering a set of hyper-parameters which would only optimize a very specific task.