Machine Learning, Deep Learning, Artificial Intelligence, NLP, AI.
article. The policy gradient theorem gives us an analytical expression for the gradient of J with respect to 𝜃: 𝜇(s) is a measure of how often the agent is likely to visit state s (across a lot of episodes) if it follows policy 𝜋. It is normalized by its sum over all states to give us a probability