The field of game theory and geometry is witnessing significant developments, with a focus on innovative frameworks and algorithms that advance our understanding of complex systems. Researchers are exploring new ways to quantify skill and chance in games, enabling principled comparisons of player influence and game balance. Furthermore, the application of machine learning and reinforcement learning techniques is leading to breakthroughs in solving imperfect-information games and exploring high-dimensional spaces. Notably, the use of embedding spaces and game-theoretic reinforcement learning is improving the quality of strategy solving and enabling the discovery of new geometric structures.

Some noteworthy papers include:

• Quantifying Skill and Chance: A Unified Framework for the Geometry of Games, which introduces a quantitative framework for separating skill and chance in games.
• No-Regret Strategy Solving in Imperfect-Information Games via Pre-Trained Embedding, which proposes a novel approach for solving strategies in imperfect-information extensive-form games using pre-trained embedding.
• Finding Kissing Numbers with Game-theoretic Reinforcement Learning, which models the Kissing Number Problem as a two-player matrix completion game and trains a game-theoretic reinforcement learning system to efficiently explore high-dimensional spaces.