Atari Safety
Summary
Atari Safety is a subtopic in AI alignment research that focuses on evaluating and ensuring the safe behavior of deep reinforcement learning (DRL) agents in Atari games. This area of study serves as a simplified yet challenging testbed for assessing AI safety in more complex domains like robotics and autonomous driving. Researchers have developed methods to analyze and improve the safety of DRL agents in Atari games, including defining safety properties, exploring all possible game traces, and implementing countermeasures such as shielding. These approaches aim to address the challenges posed by the complex and hidden dynamics of Atari games, which make traditional model-based or abstraction-based safety analysis methods unsuitable. By studying Atari Safety, researchers can gain insights into the broader challenges of ensuring AI systems behave safely in real-world applications.