Imitation Learning Behavior

Imitation Learning (IL) is a machine learning paradigm where agents learn by observing expert demonstrations rather than through trial-and-error exploration or explicit reward signals. An agent observes expert trajectories (sequences of states and actions) and learns a policy that mimics expert behavior. Core techniques include Behavior Cloning (supervised learning on demos), GAIL (adversarial imitation learning), Inverse Reinforcement Learning (inferring reward function from demos), and DAgger (iterative expert querying). IL is applied in robotics (learning manipulation from human demonstration), autonomous driving (learning from human drivers), games, and complex control tasks.