algorithm (using a table) applies only to discrete action and state spaces. Discretization of these values leads to inefficient learning, largely due to the curse of dimensionality. However, there are adaptations of Q-learning that attempt to solve this problem such as Wire-fitted Neural Network Q-Learning. See also Reinforcement learning Temporal difference learning SARSA Iterated prisoner's dilemma Game theory References External links Watkins, C.J.C.H. (1989). Learning from Delayed Rewards. PhD thesis, Cambridge University, Cambridge, England. Strehl, Li, Wiewiora, Langford, Littman (2006). PAC model-free reinforcement learning Reinforcement Learning: An Introduction by Richard Sutton and Andrew S. Barto, an online textbook. See "6.5 Q-Learning: Off-Policy TD Control". Piqle: a Generic Java Platform for Reinforcement Learning Reinforcement Learning Maze, a demonstration of guiding an ant through a maze using Q-learning Q-learning work by Gerald Tesauro Machine learning algorithms Reinforcement learning