# Autonomous Payload Thermal Control

arXiv: https://arxiv.org/abs/2307.15438 · HTML: https://mlsanity.com/paper/2307.15438/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 11 references that are in the map.
- First posted on arXiv 28 Jul 2023.

## Builds on (most cited first, 3 of 3)

- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Energy-Efficient Thermal Comfort Control in Smart Buildings via Deep Reinforcement Learning](https://mlsanity.com/paper/1901.04693/) (2019)
- [PPO Dash: Improving Generalization in Deep Reinforcement Learning](https://mlsanity.com/paper/1907.06704/) (2019)

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

- [ASTREA: Introducing Agentic Intelligence for Orbital Thermal Autonomy](https://mlsanity.com/paper/2509.13380/) (2025)

## Abstract (arXiv)

In small satellites there is less room for heat control equipment, scientific instruments, and electronic components. Furthermore, the near proximity of electronic components makes power dissipation difficult, with the risk of not being able to control the temperature appropriately, reducing component lifetime and mission performance. To address this challenge, taking advantage of the advent of increasing intelligence on board satellites, an autonomous thermal control tool that uses deep reinforcement learning is proposed for learning the thermal control policy onboard. The tool was evaluated in a real space edge processing computer that will be used in a demonstration payload hosted in the International Space Station (ISS). The experiment results show that the proposed framework is able to learn to control the payload processing power to maintain the temperature under operational ranges, complementing traditional thermal control systems.