Online Inverse Reinforcement Learning Under Occlusion
Computing Sciences and Computer Engineering
Inverse reinforcement learning (IRL) is the problem of learning the preferences of an agent from observing its behavior on a task. While this problem is witnessing sustained attention, the related problem of online IRL – where the observations are incrementally accrued, yet the real-time demands of the application often prohibit a full rerun of an IRL method – has received much less attention. We introduce a formal framework for online IRL, called incremental IRL I2RL), and a new method that advances maximum entropy IRL with hidden variables, to this setting. Our analysis shows that the new method has a monotonically improving performance with more demonstration data, as well as probabilistically bounded error, both under full and partial observability. Experiments in a simulated robotic application, which involves learning under occlusion, show the significantly improved performance of I2RL as compared to both batch IRL and an online imitation learning method.
Proceedings of the 18th International Conference On Autonomous Agents and Multiagent Systems
(2019). Online Inverse Reinforcement Learning Under Occlusion. Proceedings of the 18th International Conference On Autonomous Agents and Multiagent Systems.
Available at: https://aquila.usm.edu/fac_pubs/18068