# Revisiting Maximum Entropy Inverse Reinforcement Learning: New Perspectives and Algorithms

arXiv: https://arxiv.org/abs/2012.00889 · HTML: https://mlsanity.com/paper/2012.00889/

## Key facts

- Cited by 10 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 4 of its 23 references that are in the map.
- First posted on arXiv 1 Dec 2020.

## Placed in

- Offline RL and imitation learning from data / [Inverse reinforcement learning](https://mlsanity.com/offlinerl/inverse-reinforcement-learning/)

## Builds on (most cited first, 4 of 4)

- [OpenAI Gym](https://mlsanity.com/paper/1606.01540/) (2016)
- [Learning Robust Rewards with Adversarial Inverse Reinforcement Learning](https://mlsanity.com/paper/1710.11248/) (2017)
- [Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimization](https://mlsanity.com/paper/1603.00448/) (2016)
- [A Connection between Generative Adversarial Networks, Inverse Reinforcement Learning, and Energy-Based Models](https://mlsanity.com/paper/1611.03852/) (2016)

## Built on by (most cited first, 10 of 10)

- [Confucius3-Math: A Lightweight High-Performance Reasoning LLM for Chinese K-12 Mathematics Learning](https://mlsanity.com/paper/2506.18330/) (2025)
- [Inverse Reinforcement Learning with Just Classification and a Few Regressions](https://mlsanity.com/paper/2509.21172/) (2025)
- [X-MEN: Guaranteed XOR-Maximum Entropy Constrained Inverse Reinforcement Learning](https://mlsanity.com/paper/2203.11842/) (2022)
- [Maximum Causal Entropy IRL in Mean-Field Games and GNEP Framework for Forward RL](https://mlsanity.com/paper/2401.06566/) (2024)
- [LiMIIRL: Lightweight Multiple-Intent Inverse Reinforcement Learning](https://mlsanity.com/paper/2106.01777/) (2021)
- [Inverse Reinforcement Learning from Non-Stationary Learning Agents](https://mlsanity.com/paper/2410.14135/) (2024)
- [Can Optimal Transport Improve Federated Inverse Reinforcement Learning?](https://mlsanity.com/paper/2601.00309/) (2026)
- [Residual Chain Prediction for Autonomous Driving Path Planning](https://mlsanity.com/paper/2404.05423/) (2024)
- [DRIVE: Dynamic Rule Inference and Verified Evaluation for Constraint-Aware Autonomous Driving](https://mlsanity.com/paper/2508.04066/) (2025)
- [Diffusion-Based Data-Driven Assortment Optimization](https://mlsanity.com/paper/2608.11419/) (2026)

## Abstract (arXiv)

We provide new perspectives and inference algorithms for Maximum Entropy (MaxEnt) Inverse Reinforcement Learning (IRL), which provides a principled method to find a most non-committal reward function consistent with given expert demonstrations, among many consistent reward functions. We first present a generalized MaxEnt formulation based on minimizing a KL-divergence instead of maximizing an entropy. This improves the previous heuristic derivation of the MaxEnt IRL model (for stochastic MDPs), allows a unified view of MaxEnt IRL and Relative Entropy IRL, and leads to a model-free learning algorithm for the MaxEnt IRL model. Second, a careful review of existing inference algorithms and implementations showed that they approximately compute the marginals required for learning the model. We provide examples to illustrate this, and present an efficient and exact inference algorithm. Our algorithm can handle variable length demonstrations; in addition, while a basic version takes time quadratic in the maximum demonstration length L, an improved version of this algorithm reduces this to linear using a padding trick. Experiments show that our exact algorithm improves reward learning as compared to the approximate ones. Furthermore, our algorithm scales up to a large, real-world dataset involving driver behaviour forecasting. We provide an optimized implementation compatible with the OpenAI Gym interface. Our new insight and algorithms could possibly lead to further interest and exploration of the original MaxEnt IRL model.