Global overview of Imitation Learning

Abstract

Imitation Learning is a sequential task where the learner tries to mimic an expert's action in order to achieve the best performance. Several algorithms have been proposed recently for this task. In this project, we aim at proposing a wide review of these algorithms, presenting their main features and comparing them on their performance and their regret bounds.

9 pages, 5 figures, 5 appendix pages

References in corpus (2)

• Reinforcement and Imitation Learning via Interactive No-Regret Learning
• Active Imitation Learning via Reduction to I.I.D. Active Learning

Cited by in corpus (5)

• Attacking Vision-based Perception in End-to-End Autonomous Driving Models
• Situated GAIL: Multitask imitation using task-conditioned adversarial inverse reinforcement learning
• Transfer Learning for Prosthetics Using Imitation Learning
• Transferable Active Grasping and Real Embodied Dataset
• Augmented Q Imitation Learning (AQIL)