Reinforcement learning is a type of machine learning where an agent learns to make decisions by taking actions in an environment to achieve maximum cumulative reward. It is inspired by behavioral psychology and is used widely in robotics, gaming, and autonomous control systems.
Key concepts include:
- Agent: The learner or decision-maker.
- Environment: The external system within which the agent operates.
- Action: The choices the agent makes to interact with the environment.
- Reward: Feedback from the environment to evaluate action effectiveness.
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