Using Artificial Intelligence That Deals With Training An Agent
Different subcategories of machine learning algorithms are defined according to the way the agent receives its feedback. The three main subcategories are: supervised learning, unsupervised learning and reinforcement learning , however, this distinction is not absolute, as some algorithms have characteristics from more than one of these categories.
In supervised learning, the algorithm is trained on input-output examples, called labeled data, and its goal is to find a function that effectively maps new inputs to the desired labels. This category of algorithms is usually used for classification and regression tasks.
In unsupervised learning, the algorithm tries to discover patterns in the input data, without being given any specific instructions about the output. Such algorithms are used to perform clustering and dimensionality reduction tasks. Lastly, in reinforcement learning, the agent learns a policy on how to act, given a specific state of its environment, by receiving either a punishment or a reward for its actions by the environment. This way, it can alter its…