What is the Difference Between Bootstrapping Sampling and Stochastic Approximation in Reinforcement Learning?
Bootstrapping sampling and stochastic approximation are two important techniques in reinforcement learning. Both techniques are used to reduce variance in value estimates, but they do so in different ways.
Bootstrapping sampling is a technique that involves resampling the data from the training set to create a new training set. This new training set can then be used to train a new model, and the process can be repeated until the model converges. Bootstrapping sampling can be used to reduce variance in value estimates because it allows the model to learn from a larger and more diverse set of data.
Stochastic approximation is a technique that involves updating the value estimate using a weighted average of the current value estimate and the new value estimate. The weight of the new value estimate is determined by a learning rate parameter. Stochastic approximation can be used to reduce variance in value estimates because it allows the model to learn from new data without forgetting the old data.
In general, bootstrapping sampling is more computationally expensive than stochastic approximation, but it can lead to more accurate results. Stochastic approximation is less computationally expensive than bootstrapping sampling, but it can be more difficult to tune the learning rate parameter.
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