Reinforcement Learning, Deep Learning, Machine Learning, Neural Networks, Algorithms.
expresses the expected value of the current state as the sum of the immediate reward and the expected value of the next state. By iteratively updating the Q-values, Q-learning converges to the optimal Q-function and thus the optimal policy. One of the key advantages of Q-learning is that it can