Goal-based agents: These agents build on the information that a model-based agent stores by augmenting it with goal information or data regarding desirable outcomes and situations.
Utility-based agents: These are comparable to the goal-based agents, except they offer an extra utility measurement. This measurement rates each possible scenario based on the desired result and selects the action that maximizes the outcome. Rating criteria examples include variables such as success probability or the number of resources required.
Learning agents: These agents employ an additional learning element to gradually improve and become more knowledgeable over time about an environment. The learning element uses feedback to decide how the performance elements should be gradually changed to show improvement.
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