Every year, companies and space agencies launch hundreds of rockets into space, with this number expected to grow dramatically due to ambitious missions to the Moon, Mars, and beyond. These aspirations hinge on one critical challenge: propulsion—the methods used to propel rockets and spacecraft forward. To make interplanetary travel faster, safer, and more efficient, scientists are seeking breakthroughs in propulsion technology. Artificial intelligence (AI) is one such technology that is beginning to provide some of these necessary advancements.
A team of engineers and graduate students is exploring how AI, and a subset known as machine learning, can transform spacecraft propulsion. From optimizing nuclear thermal engines to managing complex plasma confinement in fusion systems, AI is reshaping propulsion design and operations. It is quickly becoming an indispensable partner in humankind’s journey to the stars.
Machine Learning and Reinforcement Learning
Machine learning is a branch of AI that identifies patterns in data it has not explicitly been trained on. It encompasses various branches, each emulating intelligence in different ways: by recognizing patterns, parsing and generating language, or learning from experience. This last subset, commonly known as reinforcement learning, teaches machines to perform tasks by rating their performance, enabling continuous improvement through experience.
As a simple example, consider a chess player who does not calculate every move but rather recognizes patterns from playing thousands of matches. Reinforcement learning creates similar intuitive expertise in machines and systems, but at a computational speed and scale impossible for humans. It learns through experiences and iterations by observing its environment, allowing the machine to interpret outcomes correctly and deploy the best strategies for achieving its goals.
Reinforcement learning can improve human understanding of deeply complex systems—those that challenge the limits of human intuition. It can help determine the most efficient trajectory for a spacecraft heading anywhere in space by optimizing the propulsion necessary to send the craft there. It can also potentially design better propulsion systems, from selecting the best materials to developing configurations that efficiently transfer heat between engine parts.
Reinforcement Learning for Propulsion Systems
In space propulsion, reinforcement learning generally falls into two categories: those that assist during the design phase—when engineers define mission needs and system capabilities—and those that support real-time operation once the spacecraft is in flight.
Nuclear Propulsion: The Future of Space Travel
Among the most exotic and promising propulsion concepts is nuclear propulsion, which harnesses the same forces that power atomic bombs and fuel the sun: nuclear fission and nuclear fusion. Fission works by splitting heavy atoms such as uranium or plutonium to release energy—a principle used in most terrestrial nuclear reactors. Fusion, on the other hand, merges lighter atoms such as hydrogen to produce even more energy, though it requires far more extreme conditions to initiate.
Fission is a more mature technology that has been tested in some space propulsion prototypes. It has even been used in space in the form of radioisotope thermoelectric generators, like those that powered the Voyager probes. However, fusion remains a tantalizing frontier. Nuclear thermal propulsion could one day take spacecraft to Mars and beyond at a lower cost than simply burning fuel, getting a craft there faster than electric propulsion, which uses a heated gas made of charged particles called plasma.
Unlike these systems, nuclear propulsion relies on heat generated from atomic reactions. That heat is transferred to a propellant, typically hydrogen, which expands and exits through a nozzle to produce thrust and propel the craft forward.
Reinforcement Learning’s Role in Design
Early nuclear thermal propulsion designs from the 1960s, such as those in NASA’s NERVA program, used solid uranium fuel molded into prism-shaped blocks. Since then, engineers have explored alternative configurations—from beds of ceramic pebbles to grooved rings with intricate channels. The more efficiently a reactor can transfer heat from the fuel to the hydrogen, the more thrust it generates.
This area is where reinforcement learning has proved essential. Optimizing the geometry and heat flow between fuel and propellant is a complex problem involving countless variables—from material properties to the amount of hydrogen flowing across the reactor at any moment. Reinforcement learning can analyze these design variations and identify configurations that maximize heat transfer. Imagine it as a smart thermostat for a rocket engine—one you definitely don’t want to stand too close to, given the extreme temperatures involved.
Reinforcement Learning and Fusion Technology
Reinforcement learning also plays a key role in developing nuclear fusion technology. Large-scale experiments such as the JT-60SA tokamak in Japan are pushing the boundaries of fusion energy, but their massive size makes them impractical for spaceflight. Researchers are thus exploring compact designs such as polywells. These exotic devices look like hollow cubes, a few inches across, and they confine plasma in magnetic fields to create the conditions necessary for fusion.
Controlling magnetic fields within a polywell is no small feat. The magnetic fields must be strong enough to keep hydrogen atoms bouncing around until they fuse—a process that demands immense energy to start but can become self-sustaining once underway. Overcoming this challenge is necessary for scaling this technology for nuclear thermal propulsion.
Reinforcement Learning and Energy Generation
However, reinforcement learning’s role doesn’t end with design. It can help manage fuel consumption—a critical task for missions that must adapt on the fly. In today’s space industry, there’s growing interest in spacecraft that can serve different roles depending on the mission’s needs and how they adapt to priority changes over time.
Military applications, for instance, must respond rapidly to shifting geopolitical scenarios. An example of a technology adapted to fast changes is Lockheed Martin’s LM400 satellite, which has varied capabilities such as missile warning or remote sensing. But this flexibility introduces uncertainty. How much fuel will a mission require? And when will it need it? Reinforcement learning can help with these calculations.
From bicycles to rockets, learning through experience—whether human or machine—is shaping the future of space exploration. As scientists push the boundaries of propulsion and intelligence, AI is playing a growing role in space travel. It may help scientists explore within and beyond our solar system, opening the gates for new discoveries.
