Inverse Optimal Control with Linearly-Solvable MDPs
Abstract
We present new algorithms for inverse optimal control (or inverse reinforcement learning, IRL) within the framework of linearlysolvable MDPs (LMDPs). Unlike most prior IRL algorithms which recover only the control policy of the expert, we recover the policy, the value function and the cost function. This is possible because here the cost and value functions are uniquely defined given the policy. Despite these special properties, we can handle a wide variety of problems such as the grid worlds popular in RL and most of the nonlinear problems arising in robotics and control engineering. Direct comparisons to
Cite
Text
Dvijotham and Todorov. "Inverse Optimal Control with Linearly-Solvable MDPs." International Conference on Machine Learning, 2010.Markdown
[Dvijotham and Todorov. "Inverse Optimal Control with Linearly-Solvable MDPs." International Conference on Machine Learning, 2010.](https://mlanthology.org/icml/2010/dvijotham2010icml-inverse/)BibTeX
@inproceedings{dvijotham2010icml-inverse,
title = {{Inverse Optimal Control with Linearly-Solvable MDPs}},
author = {Dvijotham, Krishnamurthy and Todorov, Emanuel},
booktitle = {International Conference on Machine Learning},
year = {2010},
pages = {335-342},
url = {https://mlanthology.org/icml/2010/dvijotham2010icml-inverse/}
}