Reinforcement learning
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Reinforcement learning (RL) is a machine learning paradigm in which an agent learns to make decisions by interacting with an environment, receiving numerical rewards or penalties based on its actions, and iteratively refining its behavior to maximize cumulative reward over time. Unlike supervised learning, RL requires no labeled training data; instead, the agent discovers effective strategies through trial and error guided by a reward signal. In robotics and AI, RL is used to train agents for complex tasks such as locomotion, dexterous manipulation, autonomous navigation, and game playing — often combining with deep neural networks (deep RL) to handle high-dimensional inputs like images and sensor streams. Multi-agent extensions enable coordinated or competitive behavior among multiple robots. RL matters because it provides a principled framework for automating the design of sophisticated control policies that are difficult or impossible to hand-engineer, enabling robots and AI systems to acquire adaptive, generalizable skills with minimal human specification of how tasks should be accomplished.
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