# Automated Feature Selection for Inverse Reinforcement Learning

arXiv: https://arxiv.org/abs/2403.15079 · HTML: https://mlsanity.com/paper/2403.15079/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Inverse reinforcement learning, one of 13 directions in Offline RL and imitation learning from data.
- Builds on 10 of its 25 references that are in the map.
- First posted on arXiv 22 Mar 2024.

## Placed in

- Offline RL and imitation learning from data / [Inverse reinforcement learning](https://mlsanity.com/offlinerl/inverse-reinforcement-learning/)
- Deep reinforcement learning methods / [RL for robot locomotion and manipulation](https://mlsanity.com/deeprl/rl-for-robot-locomotion-and-manipulation/)

## Builds on (most cited first, 10 of 10)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Soft Actor-Critic Algorithms and Applications](https://mlsanity.com/paper/1812.05905/) (2018)
- [Behavioral Cloning from Observation](https://mlsanity.com/paper/1805.01954/) (2018)
- [Learning Robust Rewards with Adversarial Inverse Reinforcement Learning](https://mlsanity.com/paper/1710.11248/) (2017)
- [Error Bounds of Imitating Policies and Environments](https://mlsanity.com/paper/2010.11876/) (2020)
- [f-IRL: Inverse Reinforcement Learning via State Marginal Matching](https://mlsanity.com/paper/2011.04709/) (2020)
- [Driving in Real Life with Inverse Reinforcement Learning](https://mlsanity.com/paper/2206.03004/) (2022)
- [Receding Horizon Inverse Reinforcement Learning](https://mlsanity.com/paper/2206.04477/) (2022)
- [Inverse Reinforcement Learning in a Continuous State Space with Formal Guarantees](https://mlsanity.com/paper/2102.07937/) (2021)
- [Inverse Reinforcement Learning: A Control Lyapunov Approach](https://mlsanity.com/paper/2104.04483/) (2021)

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

- [Inverse Reinforcement Learning for Minimum-Exposure Paths in Spatiotemporally Varying Scalar Fields](https://mlsanity.com/paper/2503.06611/) (2025)

## Abstract (arXiv)

Inverse reinforcement learning (IRL) is an imitation learning approach to learning reward functions from expert demonstrations. Its use avoids the difficult and tedious procedure of manual reward specification while retaining the generalization power of reinforcement learning. In IRL, the reward is usually represented as a linear combination of features. In continuous state spaces, the state variables alone are not sufficiently rich to be used as features, but which features are good is not known in general. To address this issue, we propose a method that employs polynomial basis functions to form a candidate set of features, which are shown to allow the matching of statistical moments of state distributions. Feature selection is then performed for the candidates by leveraging the correlation between trajectory probabilities and feature expectations. We demonstrate the approach's effectiveness by recovering reward functions that capture expert policies across non-linear control tasks of increasing complexity. Code, data, and videos are available at https://sites.google.com/view/feature4irl.