Within the field of Machine learning, reinforcement learning refers to the study of methods of magnifying the reward given by interactions with the environment with no a priori knowledge of its properties. Strongly inspired by the work developed in behavioral psychology it is essentially a trial and error approach to find the best strategy.
Consider an agent that receives an input vector – I – from a complex environment of which it knows nothing of – S – informing it of its state. Based only on that information, the agent has to make a decision regarding and action which will influence the state of the environment – A. This action will in itself change the state of the environment, which will result in a new input vector, and so on, each time also presenting the agent with the reward relative to its actions in the environment – r. The agent goal is then to find the ideal strategy which will give the highest reward expectations over time, based on previous experience.