Model-free (reinforcement learning)
In reinforcement learning, a model-free algorithm is an algorithm which does not use the transition probability distribution associated with the Markov decision process , 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. An example of a model-free algorithm is Q-learning.
