Model-free (reinforcement learning)

[1] Typical examples of model-free algorithms include Monte Carlo (MC) RL, SARSA, and Q-learning.

Instead, only experience is needed (i.e., samples of state, action, and reward), which is generated from interacting with an environment (which may be real or simulated).

Similar to MC, TD only uses experience to estimate the value function without knowing any prior knowledge of the environment dynamics.

[2] Model-free RL algorithms can start from a blank policy candidate and achieve superhuman performance in many complex tasks, including Atari games, StarCraft and Go.

Deep neural networks are responsible for recent artificial intelligence breakthroughs, and they can be combined with RL to create superhuman agents such as Google DeepMind's AlphaGo.