Reinforcement Learning is another part of Machine Learning that is gaining a lot of prestige in how it helps the machine learn from its progress.
- Supervised vs Reinforcement Learning: In Supervised Learning we have an external supervisor who has sufficient knowledge of the environment and also shares the learning with a supervisor to form a better understanding and complete the task, but since we have problems where the agent can perform so many different kind of subtasks by itself to achieve the overall objective, the presence of a supervisor is unnecessary and impractical. We can take up the example of a chess game, where the player can play tens of thousands of moves to achieve the ultimate objective. Creating a knowledge base for this purpose can be a really complicated task. Thus, it is imperative that in such tasks, the computer learn how to manage affairs by itself. It is hence more feasible and pertinent for the machine to learn from its own experience. Once the machine has started learning from its own experience, it can then gain knowledge from these experiences to implement in the future moves. This is probably the biggest and most imperative difference between the concepts of reinforcement and supervised learning. In both these learning types, there is a certain type of mapping between the output and input. But in the concept of Reinforcement Learning, there is an exemplary reward function, unlike Supervised Learning, that lets the system know about its progress down the right path.
- Reinforcement vs. Unsupervised Learning: Reinforcement Learning basically has a mapping structure that guides the machine from input to output. However, Unsupervised Learning has no such features present in it. In Unsupervised Learning, the machine focuses on the underlying task of locating the patterns rather than the mapping for progressing towards the end goal. For example, if the task for the machine is to suggest a good news update to a user, a Reinforcement Learning algorithm will look to get regular feedback from the user in question, and would then through the feedback build a reputable knowledge graph of all news related articles that the person may like. On the contrary, an Unsupervised Learning algorithm will try looking at many other articles that the person has read, similar to this one, and suggest something that matches the user’s preferences.
This article originally appeared on datasciencecentral.com. To read the full article, click here.
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