Reinforcement Learning (RL)
Machine learning approach where agents learn optimal behavior by interacting with environments and receiving rewards or penalties.
In more detail
In RL, an agent takes actions in an environment, observes results, and receives rewards or penalties. The agent learns to maximize cumulative rewards through trial and error. Applications include game playing, robotics, autonomous vehicles, and resource optimization.
Examples
- AlphaGo defeating chess masters
- Robotics learning manipulation
- Game AI (Atari, Dota 2)
- Autonomous vehicle control
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