Self-Play Reinforcement Learning under Imperfect Information in Big 2

Researchers develop a self-play reinforcement learning framework for Big 2, a four-player imperfect-information card game, demonstrating that PPO outperforms value-based methods under controlled conditions. The study reveals that entropy regularization and current-policy self-play improve agent performance, establishing Big 2 as a useful benchmark for testing deep RL in complex multi-agent environments with hidden information and variable action spaces.

This research addresses a fundamental challenge in artificial intelligence: training agents to perform effectively when critical information remains hidden from them. Big 2 serves as a controlled laboratory where researchers can isolate and test how different reinforcement learning algorithms handle the cognitive demands of imperfect-information multiplayer games—a class of problems far more representative of real-world decision-making than perfect-information games like chess or Go.

The study's comparative framework is methodologically rigorous. By fixing the environment, input representation, training budget, and evaluation protocol, researchers eliminate confounding variables that plague many RL comparisons. PPO's superiority over Monte Carlo Q-learning, SARSA, and Q-learning variants suggests that policy-gradient methods better navigate the exploration-exploitation tradeoff under imperfect information. The finding that moderate entropy regularization prevents policy collapse is particularly valuable—it indicates that maintaining stochasticity helps agents adapt to the non-stationary nature of multiplayer competition.

Current-policy self-play outperforming checkpoint self-play has practical implications for curriculum design in multi-agent RL. This result suggests that agents benefit more from facing constantly-improving opponents than from periodic exposure to prior versions, potentially reshaping how developers structure training protocols.

For the AI research community, this work establishes Big 2 as a standardized benchmark comparable to existing test beds. The structured approach enables future researchers to build incrementally on these baselines rather than starting from scratch. While this research doesn't directly impact cryptocurrency or trading systems, the methodological advances in multiplayer game-playing RL inform broader agent architecture design relevant to automated systems operating in competitive, information-asymmetric environments.

• →PPO significantly outperforms value-based RL methods in imperfect-information multiplayer games under controlled conditions
• →Entropy regularization prevents policies from becoming overly deterministic and improves performance against non-stationary opponents
• →Current-policy self-play creates stronger training curricula than checkpoint or fixed-opponent approaches in finite-budget settings
• →Big 2 establishes a useful standardized benchmark for testing deep RL algorithms under imperfect information and variable action spaces
• →Policy-gradient methods better handle exploration-exploitation tradeoffs in hidden-information environments than traditional value approximation