Copertina

Solving Pasur Using GPU-Accelerated Counterfactual Regret Minimization

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autore principale:	Baghal, Sina
Natura:	Preprint
Pubblicazione:	2025
Soggetti:	Artificial Intelligence Computer Science and Game Theory Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

GPU-Accelerated Counterfactual Regret Minimization
di: Kim, Juho
Pubblicazione: (2024)

Deep (Predictive) Discounted Counterfactual Regret Minimization
di: Xu, Hang, et al.
Pubblicazione: (2025)

Real-Time Parallel Counterfactual Regret Minimization
di: Li, Boning, et al.
Pubblicazione: (2026)

Minimizing Weighted Counterfactual Regret with Optimistic Online Mirror Descent
di: Xu, Hang, et al.
Pubblicazione: (2024)

Robust Deep Monte Carlo Counterfactual Regret Minimization: Addressing Theoretical Risks in Neural Fictitious Self-Play
di: Jaafari, Zakaria El
Pubblicazione: (2025)

Parallelizing Counterfactual Regret Minimization
di: Kim, Juho, et al.
Pubblicazione: (2026)

Next-Token Prediction and Regret Minimization
di: Mohri, Mehryar, et al.
Pubblicazione: (2026)

Regret Minimization and Convergence to Equilibria in General-sum Markov Games
di: Erez, Liad, et al.
Pubblicazione: (2022)

Accelerating Nash Equilibrium Convergence in Monte Carlo Settings Through Counterfactual Value Based Fictitious Play
di: Qi, Ju, et al.
Pubblicazione: (2023)

Optimistic Thompson Sampling for No-Regret Learning in Unknown Games
di: Li, Yingru, et al.
Pubblicazione: (2024)

Learning in Markov Games with Adaptive Adversaries: Policy Regret, Fundamental Barriers, and Efficient Algorithms
di: Nguyen-Tang, Thanh, et al.
Pubblicazione: (2024)

Do LLM Agents Have Regret? A Case Study in Online Learning and Games
di: Park, Chanwoo, et al.
Pubblicazione: (2024)

From External to Swap Regret 2.0: An Efficient Reduction and Oblivious Adversary for Large Action Spaces
di: Dagan, Yuval, et al.
Pubblicazione: (2023)

Easy as ABCs: Unifying Boltzmann Q-Learning and Counterfactual Regret Minimization
di: D'Amico-Wong, Luca, et al.
Pubblicazione: (2024)

Efficient Last-iterate Convergence Algorithms in Solving Games
di: Meng, Linjian, et al.
Pubblicazione: (2023)

LiteEFG: An Efficient Python Library for Solving Extensive-form Games
di: Liu, Mingyang, et al.
Pubblicazione: (2024)

A Policy-Gradient Approach to Solving Imperfect-Information Games with Best-Iterate Convergence
di: Liu, Mingyang, et al.
Pubblicazione: (2024)

Meta-Learning in Self-Play Regret Minimization
di: Sychrovský, David, et al.
Pubblicazione: (2025)

Regret Minimization in Stackelberg Games with Side Information
di: Harris, Keegan, et al.
Pubblicazione: (2024)

Regret Minimization in Bilateral Trade With Perturbed Markets
di: Lunghi, Anna, et al.
Pubblicazione: (2026)

Iterative Nash Policy Optimization: Aligning LLMs with General Preferences via No-Regret Learning
di: Zhang, Yuheng, et al.
Pubblicazione: (2024)

Selling Joint Ads: A Regret Minimization Perspective
di: Aggarwal, Gagan, et al.
Pubblicazione: (2024)

Combining Counterfactual Regret Minimization with Information Gain to Solve Extensive Games with Unknown Environments
di: Qiu, Chen, et al.
Pubblicazione: (2021)

Regret Minimization for Piecewise Linear Rewards: Contracts, Auctions, and Beyond
di: Bacchiocchi, Francesco, et al.
Pubblicazione: (2025)

Computational Lower Bounds for Regret Minimization in Normal-Form Games
di: Anagnostides, Ioannis, et al.
Pubblicazione: (2024)

Regret Bounds for Competitive Resource Allocation with Endogenous Costs
di: Chai, Rui
Pubblicazione: (2026)

Comparing Uniform Price and Discriminatory Multi-Unit Auctions through Regret Minimization
di: Potfer, Marius, et al.
Pubblicazione: (2025)

Efficient Last-Iterate Convergence in Regret Minimization via Adaptive Reward Transformation
di: Ren, Hang, et al.
Pubblicazione: (2025)

On the Limitations and Possibilities of Nash Regret Minimization in Zero-Sum Matrix Games under Noisy Feedback
di: Maiti, Arnab, et al.
Pubblicazione: (2023)

Learning not to Regret
di: Sychrovský, David, et al.
Pubblicazione: (2023)

An Efficient Black-Box Reduction from Online Learning to Multicalibration, and a New Route to $Φ$-Regret Minimization
di: Farina, Gabriele, et al.
Pubblicazione: (2026)

Regret Minimization in Population Network Games: Vanishing Heterogeneity and Convergence to Equilibria
di: Hu, Die, et al.
Pubblicazione: (2025)

Data-Augmented Game Starts for Accelerating Self-Play Exploration in Imperfect Information Games
di: Lanier, JB, et al.
Pubblicazione: (2026)

Meta-Inverse Reinforcement Learning for Mean Field Games via Probabilistic Context Variables
di: Chen, Yang, et al.
Pubblicazione: (2025)

Monopoly Deal: A Benchmark Environment for Bounded One-Sided Response Games
di: Wolf, Will
Pubblicazione: (2025)

Generative Social Choice: The Next Generation
di: Boehmer, Niclas, et al.
Pubblicazione: (2025)

LLM-Auction: Generative Auction towards LLM-Native Advertising
di: Zhao, Chujie, et al.
Pubblicazione: (2025)

Beyond Majority Voting: LLM Aggregation by Leveraging Higher-Order Information
di: Ai, Rui, et al.
Pubblicazione: (2025)

From Leiden to Pleasure Island: The Constant Potts Model for Community Detection as a Hedonic Game
di: Felipe, Lucas Lopes, et al.
Pubblicazione: (2025)

Envious Explore and Exploit
di: Ben-Porat, Omer, et al.
Pubblicazione: (2025)