# Multi-Agent Safe Planning with Gaussian Processes

arXiv: https://arxiv.org/abs/2008.04452 · HTML: https://mlsanity.com/paper/2008.04452/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Aug 2020.

## Built on by (most cited first, 13 of 13)

- [Exploration in Deep Reinforcement Learning: From Single-Agent to Multiagent Domain](https://mlsanity.com/paper/2109.06668/) (2021)
- [Multi-agent Reinforcement Learning: A Comprehensive Survey](https://mlsanity.com/paper/2312.10256/) (2023)
- [Influencing Towards Stable Multi-Agent Interactions](https://mlsanity.com/paper/2110.08229/) (2021)
- [Emergent Prosociality in Multi-Agent Games Through Gifting](https://mlsanity.com/paper/2105.06593/) (2021)
- [Near-Optimal Multi-Agent Learning for Safe Coverage Control](https://mlsanity.com/paper/2210.06380/) (2022)
- [Leveraging Smooth Attention Prior for Multi-Agent Trajectory Prediction](https://mlsanity.com/paper/2203.04421/) (2022)
- [Multi-Agent Inverse Reinforcement Learning in Real World Unstructured Pedestrian Crowds](https://mlsanity.com/paper/2405.16439/) (2024)
- [iPLAN: Intent-Aware Planning in Heterogeneous Traffic via Distributed Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2306.06236/) (2023)
- [Safe Model-Based Multi-Agent Mean-Field Reinforcement Learning](https://mlsanity.com/paper/2306.17052/) (2023)
- [Out of Distribution Detection via Domain-Informed Gaussian Process State Space Models](https://mlsanity.com/paper/2309.06655/) (2023)
- [Partner-Aware Algorithms in Decentralized Cooperative Bandit Teams](https://mlsanity.com/paper/2110.00751/) (2021)
- [Grid-Mapping Pseudo-Count Constraint for Offline Reinforcement Learning](https://mlsanity.com/paper/2404.02545/) (2024)
- [Resource-Efficient Distributed Recursive Gaussian Processes](https://mlsanity.com/paper/2609.26979/) (2026)

## Abstract (arXiv)

Multi-agent safe systems have become an increasingly important area of study as we can now easily have multiple AI-powered systems operating together. In such settings, we need to ensure the safety of not only each individual agent, but also the overall system. In this paper, we introduce a novel multi-agent safe learning algorithm that enables decentralized safe navigation when there are multiple different agents in the environment. This algorithm makes mild assumptions about other agents and is trained in a decentralized fashion, i.e. with very little prior knowledge about other agents' policies. Experiments show our algorithm performs well with the robots running other algorithms when optimizing various objectives.