Reinforcement Learning Part 14: Baselines, the Advantage Function, and Actor-Critic

In the previous post, we ended with a straightforward application of the policy gradient in the REINFORCE algorithm, which proves to be a useful stepping stone in our deep reinforcement learning (RL) journey. Here, we updated the parameters of the policy approximator (often a neural network) using this formula: Notice that we are using the […]