Reinforcement Learning

With the help of several trial-and-error experiences in a changing environment, a computer agent can learn to complete a task using a machine-learning technique called reinforcement learning. With the use of this learning strategy, the agent can complete the task without assistance from a human and without being explicitly programmed to do so by selecting a set of actions that will maximize a reward measure. In contrast to supervised learning, reinforcement learning uses feedback to autonomously train the agent without the use of labeled data. The agent can only learn from their experience because there is no labeled data. A particular class of problems, such as those in robotics, gaming, and other long-term endeavors, are solved using RL. The agent interacts with the environment and investigates it on their own. In reinforcement learning, an agent's main objective is to maximize positive reinforcement while doing well. It is a fundamental component of artificial intelligence, and the idea of reinforcement learning is the basis for all AI agents.

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