Inverse Reinforcement Learning
Learning the reward function that best explains an expert's observed behavior.
Overview
Inverse reinforcement learning (IRL) is the problem of inferring the reward function that an expert is implicitly optimizing, given observations of their behavior. Rather than learning from explicit rewards, IRL recovers the underlying goals and preferences that drive expert demonstrations.
Key Details
IRL is crucial for situations where specifying a reward function is difficult but expert demonstrations are available — such as autonomous driving, robotic manipulation, and AI alignment. Algorithms include Maximum Entropy IRL, Bayesian IRL, and adversarial approaches (GAIL). IRL connects to RLHF, where human preferences implicitly define a reward model for language model alignment.