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DP, Monte Carlo, and TD are methods of estimating returns. Policy gradient describes methods of learning a policy. So policy gradients serve a different purpose than the other things you mentioned. For clarity, you can use Monte Carlo or TD methods to estimate returns to construct the loss that you get your policy gradient from.


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A good example is the degree of freedom in Student's distribution: ‌ The degrees of freedom refers to the number of independent observations in a set of data. For example: When estimating a mean score or a proportion from a single sample, the number of independent observations is equal to the sample size minus one. e.g, if we have 100 observation $X_1, \...


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