# On Convex Data-Driven Inverse Optimal Control for Nonlinear, Non-stationary and Stochastic Systems

arXiv: https://arxiv.org/abs/2306.13928 · HTML: https://mlsanity.com/paper/2306.13928/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 61 references that are in the map.
- First posted on arXiv 24 Jun 2023.

## Builds on (most cited first, 16 of 16)

- [CVXPY: A Python-Embedded Modeling Language for Convex Optimization](https://mlsanity.com/paper/1603.00943/) (2016)
- [Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimization](https://mlsanity.com/paper/1603.00448/) (2016)
- [Maximum-Entropy Multi-Agent Dynamic Games: Forward and Inverse Solutions](https://mlsanity.com/paper/2110.01027/) (2021)
- [Online Markov decision processes with Kullback-Leibler control cost](https://mlsanity.com/paper/1401.3198/) (2014)
- [Probabilistic design of optimal sequential decision-making algorithms in learning and control](https://mlsanity.com/paper/2201.05212/) (2022)
- [Collision Avoidance for Dynamic Obstacles with Uncertain Predictions using Model Predictive Control](https://mlsanity.com/paper/2208.03529/) (2022)
- [On a probabilistic approach to synthesize control policies from example datasets](https://mlsanity.com/paper/2005.11191/) (2020)
- [Inverse stochastic optimal controls](https://mlsanity.com/paper/2005.11485/) (2020)
- [Inverse Optimal Control with Discount Factor for Continuous and Discrete-Time Control-Affine Systems and Reinforcement Learning](https://mlsanity.com/paper/2211.09917/) (2022)
- [An Optimal Predictive Control Strategy for COVID-19 (SARS-CoV-2) Social Distancing Policies in Brazil](https://mlsanity.com/paper/2005.10797/) (2020)
- [On the crowdsourcing of behaviors for autonomous agents](https://mlsanity.com/paper/2010.04608/) (2020)
- [Maximum Likelihood Estimation in Data-Driven Modeling and Control](https://mlsanity.com/paper/2011.00925/) (2020)
- [On the design of autonomous agents from multiple data sources](https://mlsanity.com/paper/2103.02020/) (2021)
- [On cost design in applications of optimal control](https://mlsanity.com/paper/2104.00917/) (2021)
- [On a Probabilistic Approach for Inverse Data-Driven Optimal Control](https://mlsanity.com/paper/2303.17957/) (2023)
- [Distributed Big-Data Optimization via Block-wise Gradient Tracking](https://mlsanity.com/paper/1808.07252/) (2018)

## Built on by (most cited first, 1 of 1)

- [Distributionally Robust Free Energy Principle for Decision-Making](https://mlsanity.com/paper/2503.13223/) (2025)

## Abstract (arXiv)

This paper is concerned with a finite-horizon inverse control problem, which has the goal of reconstructing, from observations, the possibly non-convex and non-stationary cost driving the actions of an agent. In this context, we present a result enabling cost reconstruction by solving an optimization problem that is convex even when the agent cost is not and when the underlying dynamics is nonlinear, non-stationary and stochastic. To obtain this result, we also study a finite-horizon forward control problem that has randomized policies as decision variables. We turn our findings into algorithmic procedures and show the effectiveness of our approach via in-silico and hardware validations. All experiments confirm the effectiveness of our approach.