Tag · 2 entries
#Inverse Reinforcement Learning
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.
Fundamentals · 8 minReinforcement Learning Primer: Imitation Learning and Inverse Reinforcement Learning
Behavior Cloning, DAgger, and inverse reinforcement learning use demonstrations when rewards are hard to write. This primer covers covariate shift, reward inference, VLA connections, evaluation, safety, and data provenance.
Fundamentals · 5 min