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Reinforcement Learning is a type of machine learning where an agent learns to make decisions by interacting with its environment and receiving feedback in the form of rewards or penalties. The goal is to maximize the cumulative reward over time, thereby learning an optimal policy for decision making. In the context of Explainable AI, Reinforcement Learning can be seen as a way to provide insights into how an agent makes decisions by examining its learned policy and the rewards it receives. By understanding the underlying mechanics and rationale behind the agent's decisions, Reinforcement Learning in this context promotes transparency and interpretability of AI systems. This aligns with the broader goals of Explainable AI to make AI more understandable and trustworthy for users.