"nash equilibrium" Papers
12 papers found
A Policy-Gradient Approach to Solving Imperfect-Information Games with Best-Iterate Convergence
Mingyang Liu, Gabriele Farina, Asuman Ozdaglar
ICLR 2025posterarXiv:2408.00751
3
citations
Equilibrium Policy Generalization: A Reinforcement Learning Framework for Cross-Graph Zero-Shot Generalization in Pursuit-Evasion Games
Runyu Lu, Peng Zhang, Ruochuan Shi et al.
NeurIPS 2025posterarXiv:2511.00811
2
citations
On Feasible Rewards in Multi-Agent Inverse Reinforcement Learning
Till Freihaut, Giorgia Ramponi
NeurIPS 2025spotlightarXiv:2411.15046
2
citations
Adaptively Perturbed Mirror Descent for Learning in Games
Kenshi Abe, Kaito Ariu, Mitsuki Sakamoto et al.
ICML 2024poster
Causal Inference from Competing Treatments
Ana-Andreea Stoica, Vivian Y. Nastl, Moritz Hardt
ICML 2024posterarXiv:2406.03422
Competition among Pairwise Lottery Contests
Xiaotie Deng, Hangxin Gan, Ningyuan Li et al.
AAAI 2024paperarXiv:2312.11953
4
citations
Human Alignment of Large Language Models through Online Preference Optimisation
Daniele Calandriello, Zhaohan Guo, REMI MUNOS et al.
ICML 2024poster
Improving Sample Efficiency of Model-Free Algorithms for Zero-Sum Markov Games
Songtao Feng, Ming Yin, Yu-Xiang Wang et al.
ICML 2024poster
Minimally Modifying a Markov Game to Achieve Any Nash Equilibrium and Value
Young Wu, Jeremy McMahan, Yiding Chen et al.
ICML 2024poster
Model-Based RL for Mean-Field Games is not Statistically Harder than Single-Agent RL
Jiawei Huang, Niao He, Andreas Krause
ICML 2024poster
Multi-Sender Persuasion: A Computational Perspective
Safwan Hossain, Tonghan Wang, Tao Lin et al.
ICML 2024posterarXiv:2402.04971
Nash Learning from Human Feedback
REMI MUNOS, Michal Valko, Daniele Calandriello et al.
ICML 2024spotlight