Editing Crowd simulation (section)

==== Algorithms ====
There are a wide variety of machine learning algorithms that can be applied to crowd simulations.{{fact|date=February 2025}}

Q-Learning is an algorithm residing under machine learning's sub field known as reinforcement learning. A basic overview of the algorithm is that each action is assigned a Q value and each agent is given the directive to always perform the action with the highest Q value. In this case learning applies to the way in which Q values are assigned, which is entirely reward based. When an agent comes in contact with a state, s, and action, a, the algorithm then estimates the total reward value that an agent would receive for performing that state action pair. After calculating this data, it is then stored in the agent's knowledge and the agent proceeds to act from there.{{fact|date=February 2025}}

The agent will constantly alter its behavior depending on the best Q value available to it. And as it explores more and more of the environment, it will eventually learn the most optimal state action pairs to perform in almost every situation.

The following function outlines the bulk of the algorithm:

:''Q(s, a) ←− r + maxaQ(s', a')''

Given a state s and action a, r and s are the reward and state after performing (s,a), and a' is the range over all the actions.<ref>{{cite journal |last1=Torrey |first1=Lisa |title=Crowd Simulation Via Multi-Agent Reinforcement Learning |journal=Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment |date=10 October 2010 |volume=6 |issue=1 |pages=89–94 |doi=10.1609/aiide.v6i1.12390 }}</ref>