The process of training machine learning algorithms to make various decisions is known as reinforcement learning. The machine interacts with a difficult environment to attempt a state in which it learns from interactions with the environment and shapes itself based on how the environment responds.
DISTINCTION BETWEEN SUPERVISED, UNSUPERVISED, AND REINFORCEMENT LEARNING?
While both supervised and reinforcement learning methods use the mapping between input and output, it is important to note that, unlike supervised learning, where the agent receives feedback in the form of the correct set of actions to perform a task, reinforcement uses rewards and punishments to signal positive and negative behaviour.
The difference between reinforcement learning and unsupervised learning is in the goals that these methods attempt to achieve. Unsupervised learning, as discussed in our article, seeks similarities and differences between data points, whereas reinforcement learning seeks to find an appropriate action model that maximizes the agent’s rewards.
REINFORCEMENT LEARNING TYPES
Positive reinforcement and negative reinforcement are the two types of reinforcement learning methods.
The process of encouraging or adding something when an expected behaviour pattern is displayed in order to increase the likelihood of the same behaviour being repeated is known as positive reinforcement learning. For example, a parent may praise his children for doing their homework (a reinforcing stimulus) (behavior).
Negative reinforcement entails increasing the likelihood of a specific behaviour recurring by removing the negative condition. For instance, when someone presses a button (behaviour) that disables a loud alarm (aversive stimulus).
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