# Safety-Aware Multi-Agent Apprenticeship Learning

arXiv: https://arxiv.org/abs/2201.08111 · HTML: https://mlsanity.com/paper/2201.08111/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Safe offline RL, one of 13 directions in Offline RL and imitation learning from data.
- Builds on 3 of its 25 references that are in the map.
- First posted on arXiv 20 Jan 2022.

## Placed in

- Offline RL and imitation learning from data / [Safe offline RL](https://mlsanity.com/offlinerl/safe-offline-rl/)

## Builds on (most cited first, 3 of 3)

- [Learning Nash Equilibrium for General-Sum Markov Games from Batch Data](https://mlsanity.com/paper/1606.08718/) (2016)
- [Safety-Aware Apprenticeship Learning](https://mlsanity.com/paper/1710.07983/) (2017)
- [Inverse Dynamic Games Based on Maximum Entropy Inverse Reinforcement Learning](https://mlsanity.com/paper/1911.07503/) (2019)

## Abstract (arXiv)

Our objective of this project is to make the extension based on the technique mentioned in the paper "Safety-Aware Apprenticeship Learning" to improve the utility and the efficiency of the existing Reinforcement Learning model from a Single-Agent Learning framework to a Multi-Agent Learning framework. Our contributions to the project are presented in the following bullet points: 1. Regarding the fact that we will add an extension to the Inverse Reinforcement Learning model from a Single-Agent scenario to a Multi-Agentscenario. Our first contribution to this project is considering the case of extracting safe reward functions from expert behaviors in a Multi-Agent scenario instead of being from the Single-Agent scenario. 2. Our second contribution is extending the Single-Agent Learning Framework to a Multi-Agent Learning framework and designing a novel Learning Framework based on the extension in the end. 3. Our final contribution to this project is evaluating empirically the performance of my extension to the Single-Agent Inverse Reinforcement Learning framework.