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Types of Learning in Agents in Artificial Intelligence
In this tutorial, we will about the types of learning in learning agents in Artificial Intelligence.
By Monika Sharma Last updated : April 15, 2023
Learning Agents as described earlier are the systems which are capable of training themselves by learning from their own actions and experiences.
Types of Learning in Agents
The Learning process in the agent is broadly classified into three types:
- Supervised Learning
- Unsupervised Learning
- 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.