Inverse Reinforcement Learning via Deep Gaussian Process

Ming Jin, Andreas Damianou, Pieter Abbeel, Costas Spanos
Proceedings of the 33rd Conference on Uncertainty in Artificial Intelligence, PMLR R15:471-480, 2017.

Abstract

We propose a new approach to inverse rein- forcement learning (IRL) based on the deep Gaussian process (deep GP) model, which is capable of learning complicated reward struc- tures with few demonstrations. Our model stacks multiple latent GP layers to learn ab- stract representations of the state feature space, which is linked to the demonstrations through the Maximum Entropy learning framework. In- corporating the IRL engine into the nonlinear latent structure renders existing deep GP infer- ence approaches intractable. To tackle this, we develop a non-standard variational approxima- tion framework which extends previous infer- ence schemes. This allows for approximate Bayesian treatment of the feature space and guards against overfitting. Carrying out rep- resentation and inverse reinforcement learn- ing simultaneously within our model outper- forms state-of-the-art approaches, as we demon- strate with experiments on standard bench- marks (“object world”,“highway driving”) and a new benchmark (“binary world”).

Cite this Paper

BibTeX
@InProceedings{pmlr-vR15-jin17a, title = {Inverse Reinforcement Learning via Deep {G}aussian Process}, author = {Jin, Ming and Damianou, Andreas and Abbeel, Pieter and Spanos, Costas}, booktitle = {Proceedings of the 33rd Conference on Uncertainty in Artificial Intelligence}, pages = {471--480}, year = {2017}, editor = {Elidan, Gal and Kersting, Kristian}, volume = {R15}, series = {Proceedings of Machine Learning Research}, month = {11--15 Aug}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r15/main/assets/jin17a/jin17a.pdf}, url = {https://proceedings.mlr.press/r15/jin17a.html}, abstract = {We propose a new approach to inverse rein- forcement learning (IRL) based on the deep Gaussian process (deep GP) model, which is capable of learning complicated reward struc- tures with few demonstrations. Our model stacks multiple latent GP layers to learn ab- stract representations of the state feature space, which is linked to the demonstrations through the Maximum Entropy learning framework. In- corporating the IRL engine into the nonlinear latent structure renders existing deep GP infer- ence approaches intractable. To tackle this, we develop a non-standard variational approxima- tion framework which extends previous infer- ence schemes. This allows for approximate Bayesian treatment of the feature space and guards against overfitting. Carrying out rep- resentation and inverse reinforcement learn- ing simultaneously within our model outper- forms state-of-the-art approaches, as we demon- strate with experiments on standard bench- marks (“object world”,“highway driving”) and a new benchmark (“binary world”).}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Inverse Reinforcement Learning via Deep Gaussian Process %A Ming Jin %A Andreas Damianou %A Pieter Abbeel %A Costas Spanos %B Proceedings of the 33rd Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2017 %E Gal Elidan %E Kristian Kersting %F pmlr-vR15-jin17a %I PMLR %P 471--480 %U https://proceedings.mlr.press/r15/jin17a.html %V R15 %X We propose a new approach to inverse rein- forcement learning (IRL) based on the deep Gaussian process (deep GP) model, which is capable of learning complicated reward struc- tures with few demonstrations. Our model stacks multiple latent GP layers to learn ab- stract representations of the state feature space, which is linked to the demonstrations through the Maximum Entropy learning framework. In- corporating the IRL engine into the nonlinear latent structure renders existing deep GP infer- ence approaches intractable. To tackle this, we develop a non-standard variational approxima- tion framework which extends previous infer- ence schemes. This allows for approximate Bayesian treatment of the feature space and guards against overfitting. Carrying out rep- resentation and inverse reinforcement learn- ing simultaneously within our model outper- forms state-of-the-art approaches, as we demon- strate with experiments on standard bench- marks (“object world”,“highway driving”) and a new benchmark (“binary world”). %Z Reissued by PMLR on 04 October 2026.
APA
Jin, M., Damianou, A., Abbeel, P. & Spanos, C.. (2017). Inverse Reinforcement Learning via Deep Gaussian Process. Proceedings of the 33rd Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R15:471-480 Available from https://proceedings.mlr.press/r15/jin17a.html. Reissued by PMLR on 04 October 2026.

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