The learning feedback

The learning feedback indicates the performance level achieved so far. This feature leads to the following distinction:

supervised learning (i.e., the feedback specifies the desired activity of the learner and the objective of learning is to match this desired action as closely as possible);

reinforcement learning (i.e., the feedback only specifies the utility of the actual activity of the learner and the objective is to maximize this utility); 

unsupervised learning (i.e., no explicit feedback is provided and the objective is to find out useful and desired activities on the basis of trial-and-error and self-organization processes).

In all three cases the learning feedback is assumed to be provided by the system environment or the agents themselves. This means that the environment or an agent providing feedback acts as a “teacher” in the case of supervised learning, as a “critic” in the case of reinforcement learning, and just as a passive “observer” in the case of unsupervised learning.

References:Learning in Multiagent Systems, Sandip Sen and Gerhard Weiss.