Editing Q-learning (section)

== Reinforcement learning ==
{{Main|Reinforcement learning}}
Reinforcement learning involves an [[Intelligent agent|agent]], a set of ''states'' <math>\mathcal{S}</math>, and a set <math>\mathcal{A}</math> of ''actions'' per state. By performing an action <math>a \in \mathcal{A}</math>, the agent transitions from state to state. Executing an action in a specific state provides the agent with a ''reward'' (a numerical score).

The goal of the agent is to maximize its total reward. It does this by adding the maximum reward attainable from future states to the reward for achieving its current state, effectively influencing the current action by the potential future reward. This potential reward is a weighted sum of [[expected value]]s of the rewards of all future steps starting from the current state.<ref name="Li-2023"/>

As an example, consider the process of boarding a train, in which the reward is measured by the negative of the total time spent boarding (alternatively, the cost of boarding the train is equal to the boarding time). One strategy is to enter the train door as soon as they open, minimizing the initial wait time for yourself. If the train is crowded, however, then you will have a slow entry after the initial action of entering the door as people are fighting you to depart the train as you attempt to board. The total boarding time, or cost, is then:

* 0 seconds wait time + 15 seconds fight time

On the next day, by random chance (exploration), you decide to wait and let other people depart first. This initially results in a longer wait time. However, less time is spent fighting the departing passengers. Overall, this path has a higher reward than that of the previous day, since the total boarding time is now:

* 5 second wait time + 0 second fight time

Through exploration, despite the initial (patient) action resulting in a larger cost (or negative reward) than in the forceful strategy, the overall cost is lower, thus revealing a more rewarding strategy.