# Safe multi-agent motion planning under uncertainty for drones using filtered reinforcement learning

arXiv: https://arxiv.org/abs/2311.00063 · HTML: https://mlsanity.com/paper/2311.00063/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 62 references that are in the map.
- First posted on arXiv 31 Oct 2023.

## Builds on (most cited first, 22 of 22)

- [Convex optimization](https://mlsanity.com/paper/2106.01946/) (2021)
- [Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments](https://mlsanity.com/paper/1706.02275/) (2017)
- [End-to-End Training of Deep Visuomotor Policies](https://mlsanity.com/paper/1504.00702/) (2015)
- [CVXPY: A Python-Embedded Modeling Language for Convex Optimization](https://mlsanity.com/paper/1603.00943/) (2016)
- [Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms](https://mlsanity.com/paper/1911.10635/) (2019)
- [End-to-End Safe Reinforcement Learning through Barrier Functions for Safety-Critical Continuous Control Tasks](https://mlsanity.com/paper/1903.08792/) (2019)
- [Motion Planning Among Dynamic, Decision-Making Agents with Deep Reinforcement Learning](https://mlsanity.com/paper/1805.01956/) (2018)
- [Safe Multi-Agent Reinforcement Learning via Shielding](https://mlsanity.com/paper/2101.11196/) (2021)
- [How Should a Robot Assess Risk? Towards an Axiomatic Theory of Risk in Robotics](https://mlsanity.com/paper/1710.11040/) (2017)
- [Linear model predictive safety certification for learning-based control](https://mlsanity.com/paper/1803.08552/) (2018)
- [Multi-agent Motion Planning for Dense and Dynamic Environments via Deep Reinforcement Learning](https://mlsanity.com/paper/2001.06627/) (2020)
- [A predictive safety filter for learning-based racing control](https://mlsanity.com/paper/2102.11907/) (2021)
- [Safe Multi-Agent Reinforcement Learning through Decentralized Multiple Control Barrier Functions](https://mlsanity.com/paper/2103.12553/) (2021)
- [Stochastic reachability of a target tube: Theory and computation](https://mlsanity.com/paper/1810.05217/) (2018)
- [Control of Multi-Agent Systems with Finite Time Control Barrier Certificates and Temporal Logic](https://mlsanity.com/paper/1808.02393/) (2018)
- [Learning-to-Fly: Learning-based Collision Avoidance for Scalable Urban Air Mobility](https://mlsanity.com/paper/2006.13267/) (2020)
- [Reactive and Risk-Aware Control for Signal Temporal Logic](https://mlsanity.com/paper/2108.13490/) (2021)
- [Decentralized Probabilistic Multi-Robot Collision Avoidance Using Buffered Uncertainty-Aware Voronoi Cells](https://mlsanity.com/paper/2201.04012/) (2022)
- [Risk-Averse RRT* Planning with Nonlinear Steering and Tracking Controllers for Nonlinear Robotic Systems Under Uncertainty](https://mlsanity.com/paper/2103.05572/) (2021)
- [Decentralized Motion Planning with Collision Avoidance for a Team of UAVs under High Level Goals](https://mlsanity.com/paper/1610.00912/) (2016)
- [Centralized Collision-free Polynomial Trajectories and Goal Assignment for Aerial Swarms](https://mlsanity.com/paper/2101.08829/) (2021)
- [Learning-'N-Flying: A Learning-based, Decentralized Mission Aware UAS Collision Avoidance Scheme](https://mlsanity.com/paper/2101.10404/) (2021)

## Built on by (most cited first, 5 of 5)

- [URPlanner: A Universal Paradigm For Collision-Free Robotic Motion Planning Based on Deep Reinforcement Learning](https://mlsanity.com/paper/2505.20175/) (2025)
- [A Graph-Based Reinforcement Learning Approach with Frontier Potential Based Reward for Safe Cluttered Environment Exploration](https://mlsanity.com/paper/2504.11907/) (2025)
- [LEARN: Learning End-to-End Aerial Resource-Constrained Multi-Robot Navigation](https://mlsanity.com/paper/2511.17765/) (2025)
- [A Safety-Constrained Reinforcement Learning Framework for Reliable Wireless Autonomy](https://mlsanity.com/paper/2602.13207/) (2026)
- [SAGE-LLM: Towards Safe and Generalizable LLM Controller with Fuzzy-CBF Verification and Graph-Structured Knowledge Retrieval for UAV Decision](https://mlsanity.com/paper/2602.23719/) (2026)

## Abstract (arXiv)

We consider the problem of safe multi-agent motion planning for drones in uncertain, cluttered workspaces. For this problem, we present a tractable motion planner that builds upon the strengths of reinforcement learning and constrained-control-based trajectory planning. First, we use single-agent reinforcement learning to learn motion plans from data that reach the target but may not be collision-free. Next, we use a convex optimization, chance constraints, and set-based methods for constrained control to ensure safety, despite the uncertainty in the workspace, agent motion, and sensing. The proposed approach can handle state and control constraints on the agents, and enforce collision avoidance among themselves and with static obstacles in the workspace with high probability. The proposed approach yields a safe, real-time implementable, multi-agent motion planner that is simpler to train than methods based solely on learning. Numerical simulations and experiments show the efficacy of the approach.