Learning through interaction with an environment using rewards and penalties.
Algorithms: Q-Learning, Deep Q Networks (DQN), Policy Gradient Methods.
Applications: Robotics, game AI (AlphaGo), autonomous vehicles.
Sub Tracks:
Model-Based Reinforcement Learning
Model-Free Reinforcement Learning
Policy-Based Reinforcement Learning
Value-Based Reinforcement Learning