arXiv cs.LGPaper
NashDreamer: Model-Based Reinforcement Learning for Zero-Sum Imperfect-Information Games
This is solid foundational work on multi-agent RL in adversarial settings, but the practical relevance for current AI builders is limited. The benchmark results are on board games, not on the systems you're likely shipping. If you're building agents that compete or negotiate in partial-information environments, this is worth reading; otherwise it's a research contribution that may pay dividends in three years.