In reinforcement learning (RL), a model-free algorithm is an algorithm which does not estimate the transition probability distribution associated with the Markov decision process (MDP), which, in RL, represents the problem to be solved. The transition probability distribution and the reward function are often collectively called the "model" of the environment, hence the name "model-free". A model-free RL algorithm can be thought of as an "explicit" trial-and-error algorithm. Typical examples of model-free algorithms include Monte Carlo (MC) RL, SARSA, and Q-learning.