arXiv:2605.28863v1 Announce Type: new Abstract: Imperfect-information multiplayer games test whether agents can act under hidden information, sparse rewards, and non-stationary opponents. We study these challenges in Big 2, a four-player imperfect-information card game. We develop a self-play RL framework for Big 2 that enables controlled comparisons between policy-gradient and value-approximating agents. Under a common environment, input representation, training budget, and evaluation protocol, PPO outperforms Monte Carlo Q approximation, SARSA, and Q-learning against random, greedy, and heur
The continuous advancements in AI research, particularly in reinforcement learning, drive ongoing efforts to tackle complex challenges like imperfect-information games.
This research contributes to developing more robust and adaptable AI agents capable of operating in real-world environments characterized by hidden information and dynamic opponents.
The explicit comparison and performance benchmarks among different self-play RL algorithms for imperfect information games like Big 2 offer clearer guidance for future AI development in strategic decision-making.
- · AI researchers
- · Game AI developers
- · Reinforcement learning algorithm developers
- · Simpler AI models in complex environments
Improved performance and efficiency of AI agents in strategic games with hidden information.
Application of these enhanced learning frameworks to real-world scenarios requiring decision-making under uncertainty, such as military strategy or financial trading.
The acceleration of autonomous agent development capable of sophisticated, human-like strategic reasoning in partially observable environments.
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Read at arXiv cs.LG