concept

Reinforcement Learning

Reinforcement Learning (RL) is a machine learning paradigm where an agent learns to make decisions by interacting with an environment to maximize cumulative rewards. It involves trial-and-error learning through actions, feedback in the form of rewards or penalties, and the development of policies to achieve long-term goals. RL is widely used in areas like robotics, game playing, autonomous systems, and recommendation engines.

Also known as: RL, Reinforcement Learning Algorithms, Deep Reinforcement Learning, Reinforcement Learning AI, Machine Learning RL
🧊Why learn Reinforcement Learning?

Developers should learn reinforcement learning when building systems that require autonomous decision-making in dynamic or uncertain environments, such as robotics, self-driving cars, or game AI. It is particularly useful for problems where explicit supervision is unavailable, and the agent must learn from experience, making it essential for applications in control systems, resource management, and personalized user interactions.

Compare Reinforcement Learning

Reinforcement Learning vs Supervised Learning→Reinforcement Learning vs Unsupervised Learning→Reinforcement Learning vs Imitation Learning→

Learning Resources

📚
Reinforcement Learning: An Introduction
book
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Deep Reinforcement Learning Course by Hugging Face
course
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Reinforcement Learning Tutorial on TensorFlow
tutorial
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OpenAI Spinning Up in Deep RL
docs
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🎬
Reinforcement Learning Explained by Two Minute Papers
video
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Related Tools

Machine LearningDeep LearningArtificial IntelligenceNeural NetworksQ Learning

Alternatives to Reinforcement Learning

Supervised LearningUnsupervised LearningImitation Learning