Improving the efficiency of Bayesian inverse reinforcement learning
Author(s)How, Jonathan P.; Michini, Bernard J.
MetadataShow full item record
Inverse reinforcement learning (IRL) is the task of learning the reward function of a Markov Decision Process (MDP) given knowledge of the transition function and a set of expert demonstrations. While many IRL algorithms exist, Bayesian IRL  provides a general and principled method of reward learning by casting the problem in the Bayesian inference framework. However, the algorithm as originally presented suffers from several inefficiencies that prohibit its use for even moderate problem sizes. This paper proposes modifications to the original Bayesian IRL algorithm to improve its efficiency and tractability in situations where the state space is large and the expert demonstrations span only a small portion of it. The key insight is that the inference task should be focused on states that are similar to those encountered by the expert, as opposed to making the naive assumption that the expert demonstrations contain enough information to accurately infer the reward function over the entire state space. A modified algorithm is presented and experimental results show substantially faster convergence while maintaining the solution quality of the original method.
DepartmentMassachusetts Institute of Technology. Aerospace Controls Laboratory; Massachusetts Institute of Technology. Department of Aeronautics and Astronautics; Massachusetts Institute of Technology. Laboratory for Information and Decision Systems
Proceedings of the 2012 IEEE International Conference on Robotics and Automation
Institute of Electrical and Electronics Engineers (IEEE)
Michini, Bernard, and Jonathan P. How. “Improving the efficiency of Bayesian inverse reinforcement learning.” In 2012 IEEE International Conference on Robotics and Automation, 3651-3656. Institute of Electrical and Electronics Engineers, 2012.
Author's final manuscript