All Simulations Are Not Equal: Simulation Reweighing for Imperfect Information Games
Imperfect information games are challenging benchmarks for artificial intelligent systems. To reason and plan under uncertainty is a key towards general AI. Traditionally, large amounts of simulations are used in imperfect information games, and they sometimes perform sub-optimally due to large state and action spaces. In this work, we propose a simulation reweighing mechanism using neural networks. It performs backwards verification to public previous actions and assign proper belief weights to the simulations from the information set of the current observation, using an incomplete state solver network (ISSN). We use simulation reweighing in the playing phase of the game contract bridge, and show that it outperforms previous state-of-the-art Monte Carlo simulation based methods, and achieves better play per decision.
Code (0)
등록된 구현이 없습니다.
Tasks
AllSimilar Papers 제목 키워드 기반
Boosting Fair Classifier Generalization through Adaptive Priority Reweighing
With the increasing penetration of machine learning applications in critical decision-making areas, calls for algorithmic fairness are more prominent. Although there have been various modalities to improve algorithmic fa…
Decision MakingFairnessValet: A Standardized Testbed of Traditional Imperfect-Information Card Games
AI algorithms for imperfect-information games are typically compared using performance metrics on individual games, making it difficult to assess robustness across game choices. Card games are a natural domain for imperf…
Secure Communications for All Users in Low-Resolution IRS-aided Systems Under Imperfect and Unknown CSI
Provisioning secrecy for all users, given the heterogeneity and uncertainty of their channel conditions, locations, and the unknown location of the attacker/eavesdropper, is challenging and not always feasible. This work…
AllAchieving Fairness at No Utility Cost via Data Reweighing with Influence
With the fast development of algorithmic governance, fairness has become a compulsory property for machine learning models to suppress unintentional discrimination. In this paper, we focus on the pre-processing aspect fo…
BIG-bench Machine LearningFairness"Security for Everyone" in Finite Blocklength IRS-aided Systems With Perfect and Imperfect CSI
Provisioning secrecy for all users, given the heterogeneity in their channel conditions, locations, and the unknown location of the attacker/eavesdropper, is challenging and not always feasible. The problem is even more …