Reinforcement Learning Foundations

Learn reinforcement learning as an engineering discipline rather than a set of demos: frame a problem as an MDP, implement both value-based and policy-gradient agents, and read the failure signatures when learning stalls or the agent games its reward. Learners train agents in Gymnasium environments, diagnose reward hacking they caused themselves, and finish able to argue honestly when bandits, forecasting or plain heuristics beat RL on a business problem.