Reinforcement Learning
Reinforcement Learning
Machine learning where an agent learns to make decisions by taking actions and receiving rewards or penalties.
In-depth explanation
In RL, an agent interacts with an environment, observing states, taking actions, and receiving rewards. The goal is to learn a policy that maximizes cumulative reward. Key concepts include exploration vs exploitation, value functions, and policy gradients. RL has achieved superhuman performance in games and is applied to robotics, recommendation, and more.
Examples
AlphaGo
Game-playing AI
Robotics control
Related terms
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