Large language models (LLMs)(Anil et al., 2023; Glaese et al., 2022; OpenAI, 2023; Ouyang
et al., 2022) have made remarkable strides in enhancing natural language understanding …

We introduce DeepNash, an autonomous agent that plays the imperfect information game
Stratego at a human expert level. Stratego is one of the few iconic board games that artificial …

Agents built with large language models (LLMs) have shown great potential across a wide
range of domains. However, in complex decision-making tasks, pure LLM-based agents …

Game theory studies the mathematical models for self-interested individuals. Nash
equilibrium is arguably the most central solution in game theory. While finding the Nash …

Actor-critic (AC) algorithms have been widely used in decentralized multi-agent systems to
learn the optimal joint control policy. However, existing decentralized AC algorithms either …

Multiplayer games have long been used as testbeds in artificial intelligence research, aptly
referred to as the Drosophila of artificial intelligence. Traditionally, researchers have focused …

Recent techniques for approximating Nash equilibria in very large games leverage neural
networks to learn approximately optimal policies (strategies). One promising line of research …

In lifelong learning an agent learns throughout its entire life without resets, in a constantly
changing environment, as we humans do. Consequently, lifelong learning comes with a …

Self-play (SP) is a popular multi-agent reinforcement learning (MARL) framework for solving
competitive games, where each agent optimizes policy by treating others as part of the …

Self-play, characterized by agents' interactions with copies or past versions of itself, has
recently gained prominence in reinforcement learning. This paper first clarifies the …