5 papers
Preference-CFR Beyond Nash Equilibrium for Better Game Strategies
Qi Ju, Thomas Tellier, Meng Sun +2
Artificial intelligence (AI) has surpassed top human players in a variety of games. In imperfect information games, these achievements have primarily been driven by Counterfactual…
Beyond Nash Equilibrium: Achieving Bayesian Perfect Equilibrium with Belief Update Fictitious Play
Qi Ju, Zhemei Fang, Yunfeng Luo
In the domain of machine learning and game theory, the quest for Nash Equilibrium (NE) in extensive-form games with incomplete information is challenging yet crucial for enhancing…
ELO-Rated Sequence Rewards: Advancing Reinforcement Learning Models
Qi Ju, Falin Hei, Zhemei Fang +1
Reinforcement Learning (RL) heavily relies on the careful design of the reward function. However, accurately assigning rewards to each state-action pair in Long-Term Reinforcement…
From First-Order to Second-Order Rationality: Advancing Game Convergence with Dynamic Weighted Fictitious Play
Qi Ju, Falin Hei, Yuxuan Liu +2
Constructing effective algorithms to converge to Nash Equilibrium (NE) is an important problem in algorithmic game theory. Prior research generally posits that the upper bound on t…
Accelerating Nash Equilibrium Convergence in Monte Carlo Settings Through Counterfactual Value Based Fictitious Play
Ju Qi, Falin Hei, Ting Feng +3
Counterfactual Regret Minimization (CFR) and its variants are widely recognized as effective algorithms for solving extensive-form imperfect information games. Recently, many impro…