Reinforcement Learning
A machine learning approach where an agent learns to make decisions by taking actions in an environment to maximize cumulative reward.
Detailed Explanation
Reinforcement Learning (RL) is a type of machine learning where an agent learns optimal behavior through trial and error, receiving rewards or penalties for its actions. Unlike supervised learning (which learns from labeled examples), RL learns from experience by exploring an environment, taking actions, and observing outcomes. The agent's goal is to learn a policy—a strategy for choosing actions—that maximizes long-term cumulative reward. RL has achieved breakthrough results in game playing (AlphaGo), robotics, and autonomous systems.
Real-World Examples
Dynamic Pricing
TravelAirlines and hotels use RL to optimize pricing in real-time based on demand, competition, and booking patterns, increasing revenue by 8-12%.
Robot Control
LogisticsWarehouse robots use RL to learn optimal paths, pick-and-place strategies, and collision avoidance, improving efficiency by 40% over programmed approaches.
Frequently Asked Questions
Q:When should I use reinforcement learning vs supervised learning?
Use RL when you have a goal but no labeled examples of correct behavior, when decisions are sequential (each action affects future states), or when you need to balance exploration vs exploitation. Use supervised learning when you have labeled training data.
Related Terms
Machine Learning (ML)
A subset of AI that enables computers to learn from data and improve their performance over time without being explicitly programmed for every scenario.
Deep Learning
A subset of machine learning that uses artificial neural networks with multiple layers to automatically learn hierarchical representations of data, enabling breakthrough performance in image, speech, and language tasks.
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