Oblivious Adversary

Oblivious adversaries model environments or opponents whose actions are predetermined and independent of the agent's choices, posing a significant challenge in areas like online learning and multi-agent systems. Current research focuses on developing algorithms and architectures, such as those based on graphical models, transformers, and online linear optimization, that achieve low regret or maintain performance despite these adversaries, particularly in federated learning and online prediction settings. Understanding the limitations and capabilities of algorithms against oblivious adversaries is crucial for designing robust and reliable AI systems, impacting fields ranging from AI safety to distributed systems.