The Reinforcement Learning Competition 2014

It has been used to model problems in automated experiment design, control, economics, game playing, scheduling, and telecommunications. The aim of the reinforcement learning competition is to encourage the development of very general learning agents for arbitrary reinforcement learning problems and to provide a test bed for the unbiased evaluation of algorithms. An agent takes actions in an unknown environment, observes their effects, and obtains rewards. The agent's aim is to learn how the environment works in order to maximize the total reward obtained during its lifetime. RL problems are quite general.