Imitation Learning by State-Only Distribution Matching

Understand

Imitation Learning from observation describes policy learning in a similar way to human learning.

• An agent's policy is trained by observing an expert performing a task.
• While many state-only imitation learning approaches are based on adversarial imitation learning, one main drawback is that adversarial training is often unstable and lacks a reliable convergence estimator.
• If the true environment reward is unknown and cannot be used to select the best-performing model, this can result in bad real-world policy performance.