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Types of Learning in Agents in Artificial Intelligence

This article is all about the types of learning agents in Artificial Intelligence. In this article, we are going to study about how many types of learning agents are there, how they all function and how the learning process is implemented in them, and in what manner they are different from each other.
Submitted by Monika Sharma, on June 17, 2019

Learning Agents as described earlier are the systems which are capable of training themselves by learning from their own actions and experiences. The Learning process in the agent is broadly classified into three types:

1. Supervised Learning
2. Unsupervised Learning
3. Reinforcement Learning

1) Supervised Learning

As the name itself suggests, in this type of learning, the agent is supervised in every means in prior itself. What it simply means is that the correct answer for almost each example problem is fed in the Knowledge Base of the system initially in its development phase. SO, whenever the agent confronts ay problem, it tries to find the same problem or a similar problem in its knowledge base whose solution it already has embedded in its system. If the problem is not there or is a lot different from those already residing in its system, then in those cases, the agent fails to function or perform any necessary action.

2) Unsupervised Learning

In the unsupervised learning agents, the answers to the problems are not available with the agent in advance. In this type of learning, the agent has to itself find the solution to the problem by learning from its past actions and experiences. However, the required information which forms the foundation of the Knowledge Base is provided to the agent in its development phase, but it has to find the solutions by itself. This type of agent is smarter than the Supervised Learning agent as it has the ability to find a relevant solution to those problems also which the agent have faced for the first time and has no prior knowledge or experience regarding it.

3) Reinforcement Learning

In the Reinforcement Learning method, the learning process is almost the same as in Unsupervised learning. But the difference is that, in Reinforcement Learning, the agent is given some reward occasionally for completing any task. Here, the goal of the agent is to get the maximum of such rewards. So, whenever any agent tries to find the solution to any problem, it searches for an alternative which would give him the maximum reward points. This type of learning not only makes the agent smart but also helps it to take the best possible decision according to the utility of the developer or the user. The utility based agents use this type of learning in their systems.