What are the advantages of using Monte Carlo Policy Gradient methods?
Monte Carlo Policy Gradient methods are a type of reinforcement learning that has a number of advantages. One advantage is that it can learn from very high-dimensional data, such as images. Additionally, Monte Carlo Policy Gradient methods can learn from data that is non-stationary, meaning that the data changes over time. Finally, Monte Carlo Policy Gradient methods are able to learn from data that is very sparse, meaning that there are only a few data points available.
