Dark Forest Theory and Multi-Agent Reinforcement Learning (2023) (hal.science)

A recent study has explored the application of Multi-Agent Reinforcement Learning (MARL) to the problem of interplanetary communication, leveraging its strengths in various fields such as robotics and game theory. Researchers created a simulated environment that mimics potential civilizations in the universe, employing MARL agents that operate under the goal of survival. This innovative approach led to findings that mirror human observations over the past century: a significant inclination towards silence among civilizations, hinting at potential strategies for avoiding detection. This research is important for the AI and machine learning community as it not only extends the utility of MARL into uncharted territories but also raises intriguing questions about the behaviors of intelligent life forms in cosmic settings. The implications are profound, as the study could inform our understanding of why we have yet to find extraterrestrial civilizations, suggesting that silence may be a tactical choice rather than mere absence. The intersection of MARL and interstellar communication could lead to new insights into human technology and the future of space exploration.