Hierarchical Apprenticeship Learning with Application to Quadruped Locomotion

We consider apprenticeship learning—learning from expert demonstrations—in the setting of large, complex domains. Past work in apprenticeship learning requires that the expert demonstrate complete trajectories through the domain. However, in many problems even an expert has difficulty controlling the system, which makes this approach infeasible. For example, consider the task of teaching a quadruped robot to navigate over extreme terrain; demonstrating an optimal policy (i.e., an optimal set of foot locations over the entire terrain) is a highly non-trivial task, even for an expert. In this paper we propose a method for hierarchical apprenticeship learning, which allows the algorithm to accept isolated advice at different hierarchical levels of the control task. This type of advice is often feasible for experts to give, even if the expert is unable to demonstrate complete trajectories. This allows us to extend the apprenticeship learning paradigm to much larger, more challenging ...

• Type: Conference
• Year: 2007
• Where: NIPS
• Authors: J. Zico Kolter, Pieter Abbeel, Andrew Y. Ng
• Keywords: Apprenticeship Learning, Demonstrate Complete Trajectories, Expert, Information Technology, NIPS 2007