# Meta-Adversarial Inverse Reinforcement Learning for Decision-making Tasks

arXiv: https://arxiv.org/abs/2103.12694 · HTML: https://mlsanity.com/paper/2103.12694/

## Key facts

- Cited by 6 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 14 of its 23 references that are in the map.
- First posted on arXiv 23 Mar 2021.

## Placed in

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

## Builds on (most cited first, 14 of 14)

- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Matching Networks for One Shot Learning](https://mlsanity.com/paper/1606.04080/) (2016)
- [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks](https://mlsanity.com/paper/1511.06434/) (2015)
- [Generative Adversarial Imitation Learning](https://mlsanity.com/paper/1606.03476/) (2016)
- [Learning to Compare: Relation Network for Few-Shot Learning](https://mlsanity.com/paper/1711.06025/) (2017)
- [On First-Order Meta-Learning Algorithms](https://mlsanity.com/paper/1803.02999/) (2018)
- [End to End Learning for Self-Driving Cars](https://mlsanity.com/paper/1604.07316/) (2016)
- [Learning Robust Rewards with Adversarial Inverse Reinforcement Learning](https://mlsanity.com/paper/1710.11248/) (2017)
- [Learning to reinforcement learn](https://mlsanity.com/paper/1611.05763/) (2016)
- [Meta-Gradient Reinforcement Learning](https://mlsanity.com/paper/1805.09801/) (2018)
- [Meta-Inverse Reinforcement Learning with Probabilistic Context Variables](https://mlsanity.com/paper/1909.09314/) (2019)
- [Learning a Prior over Intent via Meta-Inverse Reinforcement Learning](https://mlsanity.com/paper/1805.12573/) (2018)
- [Multi-task Maximum Entropy Inverse Reinforcement Learning](https://mlsanity.com/paper/1805.08882/) (2018)
- [Meta learning Framework for Automated Driving](https://mlsanity.com/paper/1706.04038/) (2017)

## Built on by (most cited first, 6 of 6)

- [Learning Safety Constraints from Demonstrations with Unknown Rewards](https://mlsanity.com/paper/2305.16147/) (2023)
- [BC-IRL: Learning Generalizable Reward Functions from Demonstrations](https://mlsanity.com/paper/2303.16194/) (2023)
- [Modeling Others' Minds as Code](https://mlsanity.com/paper/2510.01272/) (2025)
- [Improved Robustness and Safety for Pre-Adaptation of Meta Reinforcement Learning with Prior Regularization](https://mlsanity.com/paper/2108.08448/) (2021)
- [Towards Sample-Efficiency and Generalization of Transfer and Inverse Reinforcement Learning: A Comprehensive Literature Review](https://mlsanity.com/paper/2411.10268/) (2024)
- [Supervised Reward Inference](https://mlsanity.com/paper/2502.18447/) (2025)

## Abstract (arXiv)

Learning from demonstrations has made great progress over the past few years. However, it is generally data hungry and task specific. In other words, it requires a large amount of data to train a decent model on a particular task, and the model often fails to generalize to new tasks that have a different distribution. In practice, demonstrations from new tasks will be continuously observed and the data might be unlabeled or only partially labeled. Therefore, it is desirable for the trained model to adapt to new tasks that have limited data samples available. In this work, we build an adaptable imitation learning model based on the integration of Meta-learning and Adversarial Inverse Reinforcement Learning (Meta-AIRL). We exploit the adversarial learning and inverse reinforcement learning mechanisms to learn policies and reward functions simultaneously from available training tasks and then adapt them to new tasks with the meta-learning framework. Simulation results show that the adapted policy trained with Meta-AIRL can effectively learn from limited number of demonstrations, and quickly reach the performance comparable to that of the experts on unseen tasks.