science is more than an inspiration

Reinforcement learning

The basic reinforcement learning model consists of: a set of environment states ; a set of actions ; rules of transitioning between states; rules that determine the scalar immediate reward of a transition; and rules that describe what the agent observes. The rules are often stochastic. The observation typically involves the scalar immediate reward associated with the last transition. In many works, the agent is also assumed to observe the current environmental state, in which case we talk about full observability, whereas in the opposing case we talk about partial observability. Sometimes the set of actions available to the agent is restricted (e.g., you cannot spend more money than what you possess). A reinforcement learning agent interacts with its environment in discrete time steps. At each time , the agent receives an observation , which typically includes the reward . It then chooses an action  from the set of actions available, which is subsequently sent to the environment. The environment moves to a new state  and the reward associated with the transition  is determined. The goal of a reinforcement learning agent is to collect as much reward as possible. The agent can choose any action as a function of the history and it can even randomize its action selection.