Reinforcement Learning (RL)
Technical Definition
Reinforcement Learning (RL) is a type of machine learning where an agent learns to make decisions by interacting with an environment. The agent receives feedback in the form of rewards or penalties based on its actions. Over time, the agent learns to maximize its rewards by choosing the best actions in different situations. RL is particularly useful for decision-making tasks where the optimal action must be learned through trial and error. Techniques like Q-learning and deep reinforcement learning (using neural networks) are commonly used in RL.
Real-World Applications
Robotics often use reinforcement learning to teach robots how to perform tasks autonomously, such as walking, picking up objects, or navigating complex environments. In gaming, RL is used to train AI agents to play games like Go or chess, where the agent learns strategies over time through self-play.
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