mlsanityData to 7 Oct 2026

Safety-Aware Multi-Agent Apprenticeship Learning

arXiv 2201.08111 ↗First posted 20 Jan 2022cs.AI cs.LGcited by 0

On the map: Offline RL and imitation learning from data → Safe offline RL

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.

Key facts. No citing papers in the map yet (data through 7 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.

Builds on

3 of 25 references.

References · 3

  1. 2016Learning Nash Equilibrium for General-Sum Markov Games from Batch Datacited by 20
  2. 2017Safety-Aware Apprenticeship Learningcited by 12
  3. 2019Inverse Dynamic Games Based on Maximum Entropy Inverse Reinforcement Learningcited by 1

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Not cited yet.

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  1. 2026Individual Control Barrier Functions-Guided Diffusion Model for Safe Offline Multi-Agent Reinforcement Learning
  2. 2024Diffusion Models for Offline Multi-agent Reinforcement Learning with Safety Constraints
  3. 2025SafeWork-R1: Coevolving Safety and Intelligence under the AI-45circ Law
  4. 2022Safe Inverse Reinforcement Learning via Control Barrier Function
  5. 2026Safe Meta-Reinforcement Learning via Information Space Reachability
  6. 2023Learning Shared Safety Constraints from Multi-task Demonstrations
  7. 2025DIAL: Distribution-Informed Adaptive Learning of Multi-Task Constraints for Safety-Critical Systems
  8. 2022SafeAPT: Safe Simulation-to-Real Robot Learning using Diverse Policies Learned in Simulation