The Reinforcement Learning Problem

Reinforcement learning is a process where an agent learns to make decisions by interacting with an environment. It chooses actions to achieve a goal, receiving feedback called rewards or penalties based on its choices. Over time, it learns which actions lead to the best outcomes by trial and error, optimizing its behavior to maximize rewards. This approach mimics how humans and animals learn from experience, gradually improving performance on complex tasks without being explicitly programmed for every situation.