Game-Playing Agents
Overview
Game-playing agents study how AI can perceive game states, plan actions, and adapt to opponents, providing testbeds for decision-making under competition, uncertainty, and long time horizons. Common approaches include tree search and Monte Carlo Tree Search, self-play reinforcement learning, regret minimization for imperfect-information games, and language- or vision-language models equipped with memory, tools, and learned world models. Evaluation spans bounded games such as chess and Go, poker and social deduction, real-time strategy, and interactive video games, using measures such as win rate, exploitability, task progress, and performance against people. Specialized systems can be highly capable in defined settings, while broad generalization remains difficult: agents still struggle with visual grounding, reliable memory, opponent modeling, and timely action in unfamiliar or long-running games.
Latest papers
206 papers36 papers in the last four weeks, up 50% on the four weeks before. 0.2% of all new papers.