Cover Image

Analysis of Bluffing by DQN and CFR in Leduc Hold'em Poker

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Zaciragic, Tarik, Plaat, Aske, Batenburg, K. Joost
Format:	Preprint
Published:	2025
Subjects:	Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Assessing Reproducibility in Evolutionary Computation: A Case Study using Human- and LLM-based Assessment
by: Da Ros, Francesca, et al.
Published: (2026)

DexHoldem: Playing Texas Hold'em with Dexterous Embodied System
by: Chen, Feng, et al.
Published: (2026)

Research Re: search & Re-search
by: Plaat, Aske
Published: (2024)

PokerGPT: An End-to-End Lightweight Solver for Multi-Player Texas Hold'em via Large Language Model
by: Huang, Chenghao, et al.
Published: (2024)

Agentic Large Language Models, a survey
by: Plaat, Aske, et al.
Published: (2025)

How does Chain of Thought Think? Mechanistic Interpretability of Chain-of-Thought Reasoning with Sparse Autoencoding
by: Chen, Xi, et al.
Published: (2025)

Baba is LLM: Reasoning in a Game with Dynamic Rules
by: van Wetten, Fien, et al.
Published: (2025)

ACTIVA: Amortized Causal Effect Estimation via Transformer-based Variational Autoencoder
by: Sauter, Andreas, et al.
Published: (2025)

CausalPlayground: Addressing Data-Generation Requirements in Cutting-Edge Causality Research
by: Sauter, Andreas W M, et al.
Published: (2024)

CoComposer: LLM Multi-agent Collaborative Music Composition
by: Xing, Peiwen, et al.
Published: (2025)

Multi-Step Reasoning with Large Language Models, a Survey
by: Plaat, Aske, et al.
Published: (2024)

Reset-free Reinforcement Learning with World Models
by: Yang, Zhao, et al.
Published: (2024)

Chargax: A JAX Accelerated EV Charging Simulator
by: Ponse, Koen, et al.
Published: (2025)

Towards a Practical Understanding of Lagrangian Methods in Safe Reinforcement Learning
by: Spoor, Lindsay, et al.
Published: (2025)

Mirror Mode in Fire Emblem: Beating Players at their own Game with Imitation and Reinforcement Learning
by: Smid, Yanna Elizabeth, et al.
Published: (2025)

Explicitly Disentangled Representations in Object-Centric Learning
by: Majellaro, Riccardo, et al.
Published: (2024)

Cattle Trade: A Multi-Agent Benchmark for LLM Bluffing, Bidding, and Bargaining
by: Müller, Robert, et al.
Published: (2026)

Reasoning Capabilities of Large Language Models on Dynamic Tasks
by: Wong, Annie, et al.
Published: (2025)

State Design Matters: How Representations Shape Dynamic Reasoning in Large Language Models
by: Wong, Annie, et al.
Published: (2026)

Solving Deep Reinforcement Learning Tasks with Evolution Strategies and Linear Policy Networks
by: Wong, Annie, et al.
Published: (2024)

Recording and Describing Poker Hands
by: Kim, Juho
Published: (2023)

Guiding Skill Discovery with Foundation Models
by: Yang, Zhao, et al.
Published: (2025)

A Benchmark Study of Deep Reinforcement Learning Algorithms for the Container Stowage Planning Problem
by: Huang, Yunqi, et al.
Published: (2025)

PokerKit: A Comprehensive Python Library for Fine-Grained Multi-Variant Poker Game Simulations
by: Kim, Juho
Published: (2023)

CORE: Towards Scalable and Efficient Causal Discovery with Reinforcement Learning
by: Sauter, Andreas W. M., et al.
Published: (2024)

DecisionHoldem: Safe Depth-Limited Solving With Diverse Opponents for Imperfect-Information Games
by: Zhou, Qibin, et al.
Published: (2022)

PokerSkill: LLMs Can Play Expert-Level Poker without Training or Solvers
by: Li, Boning, et al.
Published: (2026)

Modified Double DQN: addressing stability
by: Halat, Shervin, et al.
Published: (2021)

A Controlled Study of Double DQN and Dueling DQN Under Cross-Environment Transfer
by: Nasir, Azkaa, et al.
Published: (2026)

PokerBench: Training Large Language Models to become Professional Poker Players
by: Zhuang, Richard, et al.
Published: (2025)

Reinforcement Learning for Sustainable Energy: A Survey
by: Ponse, Koen, et al.
Published: (2024)

DM4CT: Benchmarking Diffusion Models for Computed Tomography Reconstruction
by: Shi, Jiayang, et al.
Published: (2026)

A Hybrid Intelligence Method for Argument Mining
by: van der Meer, Michiel, et al.
Published: (2024)

Beyond Game Theory Optimal: Profit-Maximizing Poker Agents for No-Limit Holdem
by: Yi, SeungHyun, et al.
Published: (2025)

A Survey on Game Theory Optimal Poker
by: Sonawane, Prathamesh, et al.
Published: (2024)

Value of Information-Enhanced Exploration in Bootstrapped DQN
by: Plataniotis, Stergios, et al.
Published: (2025)

Readable Minds: Emergent Theory-of-Mind-Like Behavior in LLM Poker Agents
by: Lin, Hsieh-Ting, et al.
Published: (2026)

Instruction-Driven Game Engine: A Poker Case Study
by: Wu, Hongqiu, et al.
Published: (2024)

Improving the Diversity of Bootstrapped DQN by Replacing Priors With Noise
by: Meng, Li, et al.
Published: (2022)

$β$-DQN: Improving Deep Q-Learning By Evolving the Behavior
by: Zhang, Hongming, et al.
Published: (2025)