Don Horrell wrote:Hi Alexander and Brandon.
How does reinforcement learning differ from normal "gradient descent" learning?
So gradient descent is actually a particular kind of optimization strategy, just one of many ways of tuning a set of parameters of a function to be optimal according to some objective. All parametric machine learning and statistical models need to be optimized (or "fit" to data) and gradient descent is the most popular as it is scalable, iterative, and works well with big data. So we use gradient descent in RL since RL generally uses neural networks or other complex machine learning models underneath the hood, so to speak.
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