Stochastic models capture uncertainty and risk tolerance that are often present in real systems of interest . This paper extends maximum likelihood constraint inference to stochastic applications by using maximum causal entropy likelihoods . We propose an efficient algorithm that computes constraint likelihood and risktolerance in a unified Bellman backup, allowing us to generalize to Stochasticsystems without increasing computational complexity . We also propose a new Bellman back-up to the Bellman-backed algorithm, which computes constraints and risk tolerance in an efficient Bellman Backup .

Author(s) : David L. McPherson, Kaylene C. Stocking, S. Shankar Sastry

Links : PDF - Abstract

Code :

https://github.com/nhynes/abc


Coursera

Keywords : bellman - stochastic - constraint - likelihood - maximum -

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