Reinforcement Learning Primer
Reward Design Primer — Why "What to Maximize" Is the Hardest Part of RL
In reinforcement learning implementations, reward-function design more often determines the outcome than the algorithm does. This article organizes sparse vs. dense reward, the policy invariance of potential-based reward shaping, real cases of reward hacking, inverse reinforcement learning, and constrained RL.