# Revisiting Space Mission Planning: A Reinforcement Learning-Guided Approach for Multi-Debris Rendezvous

arXiv: https://arxiv.org/abs/2409.16882 · HTML: https://mlsanity.com/paper/2409.16882/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods.
- Builds on 7 of its 31 references that are in the map.
- First posted on arXiv 25 Sep 2024.

## Placed in

- Deep reinforcement learning methods / [RL for robot navigation and UAV control](https://mlsanity.com/deeprl/rl-for-robot-navigation-and-uav-control/)

## Builds on (most cited first, 7 of 7)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Reinforcement Learning for Combinatorial Optimization: A Survey](https://mlsanity.com/paper/2003.03600/) (2020)
- [A Closer Look at Invalid Action Masking in Policy Gradient Algorithms](https://mlsanity.com/paper/2006.14171/) (2020)
- [Deep Reinforcement Learning for Exact Combinatorial Optimization: Learning to Branch](https://mlsanity.com/paper/2206.06965/) (2022)
- [Neural Network-based Flight Control Systems: Present and Future](https://mlsanity.com/paper/2206.05596/) (2022)
- [A Reinforcement Learning Approach for Transient Control of Liquid Rocket Engines](https://mlsanity.com/paper/2006.11108/) (2020)
- [A Time-Dependent TSP Formulation for the Design of an Active Debris Removal Mission using Simulated Annealing](https://mlsanity.com/paper/1909.10427/) (2019)

## Built on by (most cited first, 3 of 3)

- [Optimizing Mission Planning for Multi-Debris Rendezvous Using Reinforcement Learning with Refueling and Adaptive Collision Avoidance](https://mlsanity.com/paper/2602.05075/) (2026)
- [Evaluating Robustness and Adaptability in Learning-Based Mission Planning for Active Debris Removal](https://mlsanity.com/paper/2602.05091/) (2026)
- [Optimal Multi-Debris Mission Planning in LEO: A Deep Reinforcement Learning Approach with Co-Elliptic Transfers and Refueling](https://mlsanity.com/paper/2602.17685/) (2026)

## Abstract (arXiv)

This research introduces a novel application of a masked Proximal Policy Optimization (PPO) algorithm from the field of deep reinforcement learning (RL), for determining the most efficient sequence of space debris visitation, utilizing the Lambert solver as per Izzo's adaptation for individual rendezvous. The aim is to optimize the sequence in which all the given debris should be visited to get the least total time for rendezvous for the entire mission. A neural network (NN) policy is developed, trained on simulated space missions with varying debris fields. After training, the neural network calculates approximately optimal paths using Izzo's adaptation of Lambert maneuvers. Performance is evaluated against standard heuristics in mission planning. The reinforcement learning approach demonstrates a significant improvement in planning efficiency by optimizing the sequence for debris rendezvous, reducing the total mission time by an average of approximately 10.96% and 13.66% compared to the Genetic and Greedy algorithms, respectively. The model on average identifies the most time-efficient sequence for debris visitation across various simulated scenarios with the fastest computational speed. This approach signifies a step forward in enhancing mission planning strategies for space debris clearance.