by Michele Laurelli
A machine learning paradigm where agents learn by interacting with an environment and receiving rewards or penalties.
RL agents learn optimal policies through trial and error. Key concepts include states, actions, rewards, and Q-learning. Used in game playing (AlphaGo), robotics, and autonomous systems.
AlphaGo defeating world champions
Robotic control systems
Autonomous driving decisions