Self-Play Reinforcement Learning under Imperfect Information in Big 2
This paper studies self-play reinforcement learning in the four-player imperfect-information card game Big 2. PPO outperforms Monte Carlo Q approximation, SARSA, and Q-learning against various opponents. Moderate entropy regularization improves PPO by preventing overdeterministic policies, and current-policy self-play provides a stronger finite-budget curriculum than alternatives.
Article intelligence
Key points
- Self-play RL framework developed for Big 2, a four-player imperfect-information game.
- PPO consistently outperforms value-approximating methods across opponent types.
- Entropy regularization and current-policy self-play are key to PPO's success.
- Big 2 serves as a controlled benchmark for studying imperfect information, multiplayer interaction, delayed rewards, and variable action sets.
Why it matters
This matters because self-play RL framework developed for Big 2, a four-player imperfect-information game.
Technical impact
May affect agent architecture, tool calling, workflow automation, and product integration.
[2605.28863] Self-Play Reinforcement Learning under Imperfect Information in Big 2
[Submitted on 21 May 2026]
Title:Self-Play Reinforcement Learning under Imperfect Information in Big 2
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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 heuristic Big 2 opponents. We further find that moderate entropy regularization improves PPO by preventing the policy from becoming overly deterministic, and that current-policy self-play provides a stronger finite-budget curriculum than checkpoint self-play or fixed-opponent training. Together, these results show that Big 2 is a useful controlled setting for studying deep RL under imperfect information, multiplayer interaction, delayed rewards, and variable action sets.
Comments: 11 pages
Subjects:
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
Cite as: arXiv:2605.28863 [cs.LG]
(or arXiv:2605.28863v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2605.28863
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Aalok Patwa [view email] [v1] Thu, 21 May 2026 21:56:09 UTC (1,115 KB)
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