A type of machine learning where models learn to make decisions through trial and error.
More about Reinforcement Learning:
Reinforcement Learning (RL) is a machine learning paradigm which is focused on training algorithms using a system of rewards and penalties. It imitates the fundamental way that humans and animals learn, making it suitable for a wide range of applications from gaming to autonomous vehicles.
Frequently Asked Questions
How does reinforcement learning work?
Reinforcement learning algorithms learn optimal actions through trial and error by receiving rewards for successful outcomes and penalties for unsuccessful ones.
What are some common applications of reinforcement learning?
Common applications include robotics, self-driving cars, and automated trading systems.
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