Journal of Machine Learning and Applications

Reinforcement Learning

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Rewarding desired behaviors and punishing undesirable ones are the basic tenets of the machine learning training method known as reinforcement learning. A reinforcement learning agent can typically perceive and comprehend its surroundings, act, and learn by making mistakes. The agent must decide how to explore new states while maximizing its reward to create the best possible policy through a trade-off between exploration and exploitation. The best overall action plan can entail making short-term sacrifices to balance both. As a result, the agent must gather sufficient data to ensure optimum potential in future decision-making.