AI GlossaryReinforcement LearningENTRY — Reinforcement Learning
[Reinforcement Learning]
Reinforcement Learning.
Machine learning where an agent learns to make decisions by taking actions and receiving rewards or penalties.
In-depth explanation
01In 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
02EX. 01
AlphaGo
EX. 02
Game-playing AI
EX. 03
Robotics control
More in Reinforcement Learning
0301Policy GradientPolicy Gradient methods are a class of algorithms in reinforcement learning that optimize the policy directly by using the gradient of the expected reward with respect to the policy parameters.02Q-LearningA reinforcement learning algorithm that learns the value of actions in states to determine optimal behavior.
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