How do you teach a reinforcement learning agent?

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Reinforcement learning (RL) is a branch of artificial intelligence (AI) that focuses on how agents can learn from their own actions and rewards in an environment. Unlike supervised learning, where the agent is given labeled data and feedback, or unsupervised learning, where the agent tries to find patterns and structure in unlabeled data, RL does not rely on external guidance or predefined rules. Instead, the agent learns by trial and error, exploring different actions and observing the consequences, and adjusting its behavior based on the rewards or penalties it receives. In this article, you will learn the basic concepts and steps involved in teaching a reinforcement learning agent.

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