# Satellite Chasers: Divergent Adversarial Reinforcement Learning to Engage Intelligent Adversaries on Orbit

arXiv: https://arxiv.org/abs/2409.17443 · HTML: https://mlsanity.com/paper/2409.17443/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Multi-agent reinforcement learning, one of 15 directions in Deep reinforcement learning methods.
- Builds on 14 of its 27 references that are in the map.
- First posted on arXiv 26 Sep 2024.

## Placed in

- Deep reinforcement learning methods / [Multi-agent reinforcement learning](https://mlsanity.com/deeprl/multi-agent-reinforcement-learning/)

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

- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Generative Adversarial Networks: An Overview](https://mlsanity.com/paper/1710.07035/) (2017)
- [Recent Advances in Adversarial Training for Adversarial Robustness](https://mlsanity.com/paper/2102.01356/) (2021)
- [Deep Drone Racing: From Simulation to Reality with Domain Randomization](https://mlsanity.com/paper/1905.09727/) (2019)
- [Decentralized Q-Learning in Zero-sum Markov Games](https://mlsanity.com/paper/2106.02748/) (2021)
- [Understanding Robust Overfitting of Adversarial Training and Beyond](https://mlsanity.com/paper/2206.08675/) (2022)
- [UneVEn: Universal Value Exploration for Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2010.02974/) (2020)
- [When Is Generalizable Reinforcement Learning Tractable?](https://mlsanity.com/paper/2101.00300/) (2021)
- [A Self-Play Posterior Sampling Algorithm for Zero-Sum Markov Games](https://mlsanity.com/paper/2210.01907/) (2022)
- [Empirical Analysis of Overfitting and Mode Drop in GAN Training](https://mlsanity.com/paper/2006.14265/) (2020)
- [An Introduction to Pursuit-evasion Differential Games](https://mlsanity.com/paper/2003.05013/) (2020)
- [AdverSAR: Adversarial Search and Rescue via Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2212.10064/) (2022)
- [RPM: Generalizable Behaviors for Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2210.09646/) (2022)
- [Accelerate Multi-Agent Reinforcement Learning in Zero-Sum Games with Subgame Curriculum Learning](https://mlsanity.com/paper/2310.04796/) (2023)

## Built on by (most cited first, 1 of 1)

- [A Comprehensive Survey on Multi-Agent Cooperative Decision-Making: Scenarios, Approaches, Challenges and Perspectives](https://mlsanity.com/paper/2503.13415/) (2025)

## Abstract (arXiv)

As space becomes increasingly crowded and contested, robust autonomous capabilities for multi-agent environments are gaining critical importance. Current autonomous systems in space primarily rely on optimization-based path planning or long-range orbital maneuvers, which have not yet proven effective in adversarial scenarios where one satellite is actively pursuing another. We introduce Divergent Adversarial Reinforcement Learning (DARL), a two-stage Multi-Agent Reinforcement Learning (MARL) approach designed to train autonomous evasion strategies for satellites engaged with multiple adversarial spacecraft. Our method enhances exploration during training by promoting diverse adversarial strategies, leading to more robust and adaptable evader models. We validate DARL through a cat-and-mouse satellite scenario, modeled as a partially observable multi-agent capture the flag game where two adversarial ``cat" spacecraft pursue a single ``mouse" evader. DARL's performance is compared against several benchmarks, including an optimization-based satellite path planner, demonstrating its ability to produce highly robust models for adversarial multi-agent space environments.