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reinforcement learning (RL)

Reinforcement Learning (RL) is one of three machine learning techniques. The other two methods are Supervised Learning and Unsupervised Learning

. Unlike the other two methods, reinforcement learning is concerned with the evaluation of reactions. Correct reactions are evaluated positively, while incorrect ones are evaluated negatively. Transferred to image recognition, a positive evaluation occurs when reinforcement learning has correctly recognized an object

and can assign it to it.For example, if the system correctly recognizes an apple in a fruit bowl, then this is evaluated positively. As with the other two methods, reinforcement learning also involves a learning process. In this learning process, an agent

determines the environment to which it will respond with a positive or negative action. Depending on how the environment reacts to the response, it is incorporated into future decisions. If the evaluation is positive, a reinforcement takes place, whereby the decision process is strengthened; if the evaluation is negative, future decisions will change. The algorithms for reinforcement learning take place in the Markov process, taking into account the environment and actions.

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Englisch: reinforcement learning - RL
Updated at: 03.05.2019
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