Machine Learning, AI, Reinforcement Learning, Optimization, Neural Networks.
trainable parameters for our PG model. Then, p denotes the probability of selecting action a, and r is the “reward” received for that action. We then showed that: The above equation tells us how to update our PG model, given a set of actions and their observed rewards. For discrete hyperparameters,