R250 Imitation Learning

Imitation learning was initially proposed in robotics as a way to better robots (Schaal, 1999). The connecting theme is to combine the reward function in the end of the action sequence with demonstrations of the task in hand by an expert. Since then it has been applied to a number of tasks which can be modelled as a sequence of actions taken by an agent. These include the video game agents, moving cameras to track players and structured prediction in various tasks in natural language processing.

Over the years there has been a number of algorithms proposed, in the literature but without necessarily making the connections between the various approaches clear. The initial lecture will set the criteria to be used to examine the algorithms with.

The papers presented in the 2019 version of the topic were:

• Search-based Structured Prediction Hal Daumé III, John Langford and Daniel Marcu Machine Learning Journal (MLJ), 2009

• A Reduction of Imitation Learning and Structured Prediction to No-Regret Online Learning Stephane Ross, Geoffrey J. Gordon, J. Andrew Bagnell Artificial Intelligence and Statistics Conference (AISTATS), 2011

• Learning to search better than your teacher Kai-Wei Chang, Akshay Krishnamurthy, Alekh Agarwal, Hal Daumé III and John Langford International Conference on Machine Learning (ICML), 2015

• Sequence Level Training with Recurrent Neural Networks Marc’Aurelio Ranzato, Sumit Chopra, Michael Auli, Wojciech Zaremba International Conference on Machine Learning (ICLR), 2016

• Residual Loss Prediction: Reinforcement Learning with no Incremental Feedback Hal Daumé III, John Langford and Amr Sharaf International Conference on Machine Learning (ICLR), 2018