Global overview of Imitation Learning

2018/01/19 by Alexandre Attia, Attia, Alexandre, Sharone Dayan +1 · 1 voice · 3 citations
Computer Science · Decision Sciences · Mathematics · #Advanced Bandit Algorithms Research #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and Algorithms #Reinforcement Learning in Robotics #cs.LG #stat.ML

paper · pdf · doi:10.48550/arxiv.1801.06503

openalex publication_date 2018/01/19 · arxiv published 2018/01/19 · arxiv updated 2018/01/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Citations

• Reinforcement and Imitation Learning via Interactive No-Regret Learning

Cited by

• A Review of Learning-Based Motion Planning: Toward a Data-Driven Optimal Control Approach
• Medical Vision Language Models as Policies for Robotic Surgery
• Navigation by Imitation in a Pedestrian-Rich Environment

Discussions

• Global overview of Imitation Learning [hn, 3 points, 0 comments]

Related

• Imitation Learning: Progress, Taxonomies and Challenges
• Imitation Learning in the Deep Learning Era: A Novel Taxonomy and Recent Advances
• A Survey of Imitation Learning Methods, Environments and Metrics
• The Past and Present of Imitation Learning: A Citation Chain Study
• On Value Discrepancy of Imitation Learning