No-Regret Strategy Solving in Imperfect-Information Games via Pre-Trained Embedding

• URL: http://arxiv.org/abs/2511.12083v1
• Date: Sat, 15 Nov 2025 08:04:19 GMT
• Title: No-Regret Strategy Solving in Imperfect-Information Games via Pre-Trained Embedding
• Abstract summary: This paper proposes Embedding CFR, a novel approach for solving strategies in IIEFGs within an embedding space.<n>The algorithm pre-trains and embeds features of isolated information sets into an interconnected low-dimensional continuous space.<n> Experiments on poker show that with the same spatial overhead, Embedding CFR achieves significantly faster exploitability convergence.
• Score: 23.90678135513633
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: High-quality information set abstraction remains a core challenge in solving large-scale imperfect-information extensive-form games (IIEFGs)-such as no-limit Texas Hold'em-where the finite nature of spatial resources hinders strategy solving over the full game. State-of-the-art AI methods rely on pre-trained discrete clustering for abstraction, yet their hard classification irreversibly loses critical information: specifically, the quantifiable subtle differences between information sets-vital for strategy solving-thereby compromising the quality of such solving. Inspired by the word embedding paradigm in natural language processing, this paper proposes the Embedding CFR algorithm, a novel approach for solving strategies in IIEFGs within an embedding space. The algorithm pre-trains and embeds features of isolated information sets into an interconnected low-dimensional continuous space, where the resulting vectors more precisely capture both the distinctions and connections between information sets. Embedding CFR presents a strategy-solving process driven by regret accumulation and strategy updates within this embedding space, with accompanying theoretical analysis verifying its capacity to reduce cumulative regret. Experiments on poker show that with the same spatial overhead, Embedding CFR achieves significantly faster exploitability convergence compared to cluster-based abstraction algorithms, confirming its effectiveness. Furthermore, to our knowledge, it is the first algorithm in poker AI that pre-trains information set abstractions through low-dimensional embedding for strategy solving.

Related papers

• Constraints Matrix Diffusion based Generative Neural Solver for Vehicle Routing Problems [16.541111954252397]
  We propose a novel fusion neural network framework that employs a discrete noise graph diffusion model to learn the underlying constraints of vehicle routing problems.<n>This work represents the first comprehensive experimental investigation of neural network model solvers across a 378-combinatorial space.
  arXiv  Detail & Related papers  (2026-03-08T10:03:34Z)
• RESIST: Resilient Decentralized Learning Using Consensus Gradient Descent [11.22833419439317]
  Empirical robustness risk (ERM) is a cornerstone of modern machine learning (ML)<n>This paper focuses on the man-in-the-middle (MITM) attack, which can cause models to deviate significantly from their intended ERM solutions.<n>We propose RESIST, an algorithm designed to be robust against adversarially compromised communication links.
  arXiv  Detail & Related papers  (2025-02-11T21:48:10Z)
• Learning for Cross-Layer Resource Allocation in MEC-Aided Cell-Free Networks [71.30914500714262]
  Cross-layer resource allocation over mobile edge computing (MEC)-aided cell-free networks can sufficiently exploit the transmitting and computing resources to promote the data rate.<n>Joint subcarrier allocation and beamforming optimization are investigated for the MEC-aided cell-free network from the perspective of deep learning.
  arXiv  Detail & Related papers  (2024-12-21T10:18:55Z)
• Point Cloud Understanding via Attention-Driven Contrastive Learning [64.65145700121442]
  Transformer-based models have advanced point cloud understanding by leveraging self-attention mechanisms. PointACL is an attention-driven contrastive learning framework designed to address these limitations. Our method employs an attention-driven dynamic masking strategy that guides the model to focus on under-attended regions.
  arXiv  Detail & Related papers  (2024-11-22T05:41:00Z)
• Anti-Collapse Loss for Deep Metric Learning Based on Coding Rate Metric [99.19559537966538]
  DML aims to learn a discriminative high-dimensional embedding space for downstream tasks like classification, clustering, and retrieval. To maintain the structure of embedding space and avoid feature collapse, we propose a novel loss function called Anti-Collapse Loss. Comprehensive experiments on benchmark datasets demonstrate that our proposed method outperforms existing state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-07-03T13:44:20Z)
• CHARME: A chain-based reinforcement learning approach for the minor embedding problem [10.896395182395601]
  We propose a novel approach utilizing Reinforcement Learning (RL) techniques to address the minor embedding problem, named CHARME.<n>CHARME includes three key components: a Graph Neural Network (GNN) architecture for policy modeling, a state transition algorithm that ensures validity solution, and an order exploration strategy for effective training.<n>In particular, CHARME yields superior solutions in terms of qubit usage compared to fast embedding methods such as Minorminer and ATOM.
  arXiv  Detail & Related papers  (2024-06-11T10:12:10Z)
• Hyperspectral Image Analysis in Single-Modal and Multimodal setting using Deep Learning Techniques [1.2328446298523066]
  Hyperspectral imaging provides precise classification for land use and cover due to its exceptional spectral resolution. However, the challenges of high dimensionality and limited spatial resolution hinder its effectiveness. This study addresses these challenges by employing deep learning techniques to efficiently process, extract features, and classify data in an integrated manner.
  arXiv  Detail & Related papers  (2024-03-03T15:47:43Z)
• CasCIFF: A Cross-Domain Information Fusion Framework Tailored for Cascade Prediction in Social Networks [4.480256642939794]
  Cross-Domain Information Fusion Framework (CasCIFF) is tailored for information cascade prediction. This framework exploits multi-hop neighborhood information to make user embeddings robust. In particular, the CasCIFF seamlessly integrates the tasks of user classification and cascade prediction into a consolidated framework.
  arXiv  Detail & Related papers  (2023-08-09T13:52:41Z)
• Large-scale Fully-Unsupervised Re-Identification [78.47108158030213]
  We propose two strategies to learn from large-scale unlabeled data. The first strategy performs a local neighborhood sampling to reduce the dataset size in each without violating neighborhood relationships. A second strategy leverages a novel Re-Ranking technique, which has a lower time upper bound complexity and reduces the memory complexity from O(n2) to O(kn) with k n.
  arXiv  Detail & Related papers  (2023-07-26T16:19:19Z)
• Hierarchical Deep Counterfactual Regret Minimization [53.86223883060367]
  In this paper, we introduce the first hierarchical version of Deep CFR, an innovative method that boosts learning efficiency in tasks involving extensively large state spaces and deep game trees. A notable advantage of HDCFR over previous works is its ability to facilitate learning with predefined (human) expertise and foster the acquisition of skills that can be transferred to similar tasks.
  arXiv  Detail & Related papers  (2023-05-27T02:05:41Z)
• From Contextual Data to Newsvendor Decisions: On the Actual Performance of Data-Driven Algorithms [8.89658755359509]
  We study how the relevance/quality and quantity of past data influence performance by analyzing a contextual Newsvendor problem.<n>We analyze the performance of data-driven algorithms through a notion of context-dependent worst-case expected regret.
  arXiv  Detail & Related papers  (2023-02-16T17:03:39Z)
• Contextual Model Aggregation for Fast and Robust Federated Learning in Edge Computing [88.76112371510999]
  Federated learning is a prime candidate for distributed machine learning at the network edge. Existing algorithms face issues with slow convergence and/or robustness of performance. We propose a contextual aggregation scheme that achieves the optimal context-dependent bound on loss reduction.
  arXiv  Detail & Related papers  (2022-03-23T21:42:31Z)
• Evolutionary Training and Abstraction Yields Algorithmic Generalization of Neural Computers [26.829064000815272]
  A key feature of intelligent behaviour is the ability to learn abstract strategies that scale and transfer to unfamiliar problems. We present the Neural Harvard Computer, a memory-augmented network based architecture. We show that the NHC reliably learns algorithmic solutions with strong generalization and abstraction.
  arXiv  Detail & Related papers  (2021-05-17T15:37:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.