Learning Techniques

Learning Techniques:

Q-Learning:

Q-Learning is a reinforcement learning technique that is used in machine learning. It is an algorithm that that does not need a model of its environment and can be used on-line. (Watkins, 1989) Therefore, Q-Learning is very suited for repeated games against an unknown opponent. This is mainly used in pathfinding where the reward is higher for points connected to the goal. This basically means an AI with Q-learning will keep going through this process via trial-and-error until it finds the optimal route/the highest reward. In laymen terms, the basic premise is that the algorithm is taught to take certain actions based on prior experience by rewarding or punishing actions. Similar to teaching a dog to sit by giving it treats for good behaviour. (D, 2018)

Artificial Neural Networks:

An Artificial Neural Network (ANN) is a computational model based on the structure and functions of biological neural networks. Information that flows through the network affects the structure of the Artificial Neural Network because a neural network changes - or learns, in a sense based on that input and output. An Artificial Neural Network has several advantages but one of the most recognized of these is the fact that it can learn from observing data sets. In this way, Artificial Neural Networks are used as a random function approximation tool. (Techopedia, 2018)

In terms of games, an Artificial Neural Network works by taking information and then processes that information through hidden layers, which it then translates and decides what the AI needs to do next in response. Basically, it gives the AI its own thinking brain and it is used to think just like a human player. Additionally, it has its own decision tree and it can learn from its previous experiences, for example; For example, if the Enemy AI is wandering around and bumps into the Player AI it’ll then process different actions it can take and decides upon an outcome on how to proceed.