Lecture
Policy Estimation: The Log-Likelihood Trick
Description
This lecture covers the log-likelihood trick for policy estimation, explaining the mathematical formulas and calculations involved in updating the policy weights based on rewards. It also delves into policy gradient estimation using the sample average as a Monte Carlo approximation, providing insights into fast gradient approximations. The instructor uses examples to illustrate the concepts, including neuron responses and reward calculations.
Official source
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