Monte Carlo Tree Search Satellite Scheduling Under Cloud Cover Uncertainty

The document explores the multi-satellite collection scheduling problem (m-SatCSP) and evaluates the effectiveness of using Monte Carlo Tree Search (MCTS) as an algorithmic framework for optimizing task scheduling over a constellation of satellites under uncertain conditions such as cloud cover, demonstrating competitive performance compared to existing methods.

Abstract

Efficient utilization of satellite resources in dynamic environments remains a challenging problem in satellite scheduling. This paper addresses the multi-satellite collection scheduling problem (m-SatCSP), aiming to optimize task scheduling over a constellation of satellites under uncertain conditions such as cloud cover. Leveraging Monte Carlo Tree Search (MCTS), a stochastic search algorithm, two versions of MCTS are explored to schedule satellites effectively. Hyperparameter tuning is conducted to optimize the algorithm's performance. Experimental results demonstrate the effectiveness of the MCTS approach, outperforming existing methods in both solution quality and efficiency. Comparative analysis against other scheduling algorithms showcases competitive performance, positioning MCTS as a promising solution for satellite task scheduling in dynamic environments.

Method

The authors employed the Monte Carlo Tree Search (MCTS) algorithm to efficiently and effectively schedule satellite tasks under the multi-satellite collection scheduling problem (m-SatCSP). They conducted experiments to fine-tune the hyperparameters of the MCTS algorithm, specifically focusing on the exploration coefficient and the number of simulations. The performance of the MCTS algorithm was evaluated and compared to other scheduling algorithms found in the current literature, considering the time taken to form a solution and the value of the solution. Additionally, the authors utilized test sets from previous studies to assess the algorithm's performance across various scenarios, and the experiments were conducted using a specific hardware and software setup.

Main Finding

The authors discovered that the Monte Carlo Tree Search (MCTS) algorithm, when fine-tuned with appropriate hyperparameters, demonstrated competitive performance in efficiently and effectively scheduling satellite tasks under the multi-satellite collection scheduling problem (m-SatCSP). Through experimentation and comparison with other algorithms in the literature, the MCTS algorithm generally performed at the same level or better in terms of both speed and quality of results. However, the algorithm's stochastic nature made it inconsistent in finding solutions in more dense and challenging situations. Despite this, the MCTS algorithm showed promise as a scheduling algorithm, particularly when optimized and tested further.

Conclusion

The conclusion of the document is that the Monte Carlo Tree Search (MCTS) algorithm has been explored as an effective algorithmic framework for addressing the multi-satellite collection scheduling problem (m-SatCSP). Through hyperparameter optimization, it was found that a higher number of simulations and a higher exploration coefficient generally yielded better results. The MCTS algorithm generally performed at the same level or better than other algorithms in terms of both speed and quality of results. However, the algorithm's stochastic nature made it inconsistent in finding solutions in more dense and challenging situations. Despite this, with further optimization and testing, the MCTS algorithm shows promise as a scheduling algorithm for satellite tasks in dynamic environments.

Keywords

Tabular Data Synthesis, User Needs, Tool Capabilities, Probabilistic Graphical Models, Deep Learning, Generative Models, Synthetic Data Evaluation Metrics, Hybrid Models, Data Utility, Integrity Constraints, Privacy, Differential Privacy, Evaluation of Synthetic Data