Lecture
Deep Reinforcement Learning: Policy Gradient and Value Function
Description
This lecture covers the implementation of the REINFORCE algorithm with a baseline using a neural network with an actor-critic architecture. It explains how to update policy parameters to maximize return, calculate gradients, subtract a reward baseline, and learn two neural networks for actions and value functions.
Official source
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