This package contains the algorithms of IRL when the behavior data is provided by multiple experts. The experiments are conducted in gridworld and highway problem. - The Spectral algorithms are still under test. - The DPM algorithms are described in [ChoiKim.12]. - The EM algorithms are described in [BabesETAL.11]. # Requirement This package was built with Matlab R2013a. # Package overview - Spectral: merge the feature expectation based on SVD to build TF matrix and build cluster by reward matrix using the idea of pseudo inverse. - BIRL: Finding a maximum-a-posterior (MAP) estimate in Bayesian framework for IRL [ChoiKim.11]. - DPM-BIRL: Extending BIRL with Dirichlet process mixture model to address IRL problems for multiple experts [ChoiKim.12]. - EM_IRL: Using expectation-maximization method to address IRL problems for multiple experts [BabesETAL.11]. - Ind_BIRL: Using MAP inference for BIRL on each trajectory to address IRL problems for multiple experts. # Usage Use the scripts whose filename starts with "run". [ChoiKim.11] J. Choi and K. Kim, MAP inference for Bayesian inverse reinforcement learning, NIPS 2010. [ChoiKim.12] J. Choi and K. Kim, Nonparametric Bayesian inverse reinforcement learning for multiple reward functions, NIPS 2011. [BabesETAL.11] M. Babes-Vroman, V. Marivate, K. Subramanian, M. L. Littman, Apprenticeship learning about multiple intentions, ICML 2011.