In reinforcement learning (RL), the term self-play describes a kind of multi-agent learning
(MAL) that deploys an algorithm against copies of itself to test compatibility in various …

Reinforcement learning problems with multiple agents pose the challenge of efficiently
adapting to nonstationary dynamics arising from other agents' strategic behavior. Although …

Multi-armed bandits (MAB) is an online learning and decisionmaking model under
uncertainty. Instead of maximizing the expected utility (or reward) in a classical MAB setting …

Despite recent advancements in AI and NLP, negotiation remains a difficult domain for AI
agents. Traditional game theoretic approaches that have worked well for two-player zero …

We study the problem of adaptability in repeated games: simultaneously guaranteeing low
regret for several classes of opponents. We add the constraint that our algorithm is non …

Artificial Intelligence is increasingly being used in real-life applications such as driving with
autonomous cars; deliveries with autonomous drones; customer support with chat-bots; …