Reinforcement Learning is a type of ML where an agent learns to make decisions by performing actions and receiving rewards or penalties. It mimics how humans learn through trial and error and is particularly effective for sequential decision-making tasks.

NYD Application: Could optimize our deployment strategies and automated testing sequences by learning from successful and failed outcomes.

Example: "The RL agent learned to optimize our CI/CD pipeline by receiving rewards for faster, error-free deployments."