Imitation Learning

Imitation learning is a framework for learning behavioral strategies from demonstrations, where demonstration data is typically presented in the form of state-action trajectories. This method aims to establish a generalizable mapping from states to actions through supervised learning (Behavior Cloning), or to find a reward/cost function that optimizes the decisions in the demonstrations via inverse reinforcement learning (Inverse Reinforcement Learning). The latest inverse Q-learning methods learn the Q-function directly from expert data, implicitly representing the reward, and thus provide the optimal policy in the form of a Boltzmann distribution. Imitation learning has significant application value in robotics, autonomous driving, and other fields, effectively enhancing the decision-making capabilities and execution efficiency of systems.