Reinforcement Learning

RL is used in robotics, game playing (AlphaGo), recommendation systems, autonomous vehicles, resource optimization, and training AI agents for complex decision-making tasks.

RL has a steeper learning curve due to concepts like reward shaping, exploration vs exploitation, and environment design. Strong math foundations in probability and optimization help.

Basic RL concepts like Q-learning and bandits can be learned independently, but modern deep RL methods require understanding of neural networks and deep learning.

OpenAI Gymnasium (formerly Gym), Unity ML-Agents, and MuJoCo are popular environments. Many courses include pre-built environments for hands-on experimentation.