Reinforcement Learning, Deep Learning, Machine Learning, Neural Networks, Algorithms.
function. The policy network is updated using the policy gradient, while the value network is updated using the temporal difference (TD) error, which is the difference between the predicted and actual rewards. Policy gradient methods have several advantages over value-based methods like Q-learning,