Related papers: Resource-Efficient Model-Free Reinforcement Learning for Board Games

Resource-Efficient Model-Free Reinforcement Learning for Board Games

URL: http://arxiv.org/abs/2602.10894v1
Date: Wed, 11 Feb 2026 14:25:38 GMT
Title: Resource-Efficient Model-Free Reinforcement Learning for Board Games
Authors: Kazuki Ota, Takayuki Osa, Motoki Omura, Tatsuya Harada,
Abstract summary: We propose a model-free reinforcement learning algorithm designed for board games to achieve more efficient learning.<n>To validate the efficiency of the proposed method, we conducted comprehensive experiments on five board games: Animal Shogi, Gardner Chess, Go, Hex, and Othello.
Score: 41.616970332107584
License: http://creativecommons.org/licenses/by/4.0/
Abstract: Board games have long served as complex decision-making benchmarks in artificial intelligence. In this field, search-based reinforcement learning methods such as AlphaZero have achieved remarkable success. However, their significant computational demands have been pointed out as barriers to their reproducibility. In this study, we propose a model-free reinforcement learning algorithm designed for board games to achieve more efficient learning. To validate the efficiency of the proposed method, we conducted comprehensive experiments on five board games: Animal Shogi, Gardner Chess, Go, Hex, and Othello. The results demonstrate that the proposed method achieves more efficient learning than existing methods across these environments. In addition, our extensive ablation study shows the importance of core techniques used in the proposed method. We believe that our efficient algorithm shows the potential of model-free reinforcement learning in domains traditionally dominated by search-based methods.

Related papers

Sequencing to Mitigate Catastrophic Forgetting in Continual Learning [1.1724961392643483]
Catastrophic forgetting (CF) is a major challenge to the progress of Continual Learning approaches.<n>We consider the role of task sequencing in mitigating CF and propose a method for determining the optimal task order.<n>Results demonstrate that intelligent task sequencing can substantially reduce CF.
arXiv Detail & Related papers (2025-12-18T18:40:58Z)
Enfoque Odychess: Un método dialéctico, constructivista y adaptativo para la enseñanza del ajedrez con inteligencias artificiales generativas [0.0]
The Odychess Approach represents an effective pedagogical methodology for teaching chess.<n>The implications of this work are relevant for educators and institutions interested in adopting innovative pedagogical technologies.
arXiv Detail & Related papers (2025-05-10T13:58:47Z)
Scaling of Search and Learning: A Roadmap to Reproduce o1 from Reinforcement Learning Perspective [77.94874338927492]
OpenAI has claimed that the main techinique behinds o1 is the reinforcement learning.<n>This paper analyzes the roadmap to achieving o1 from the perspective of reinforcement learning.
arXiv Detail & Related papers (2024-12-18T18:24:47Z)
RLIF: Interactive Imitation Learning as Reinforcement Learning [56.997263135104504]
We show how off-policy reinforcement learning can enable improved performance under assumptions that are similar but potentially even more practical than those of interactive imitation learning. Our proposed method uses reinforcement learning with user intervention signals themselves as rewards. This relaxes the assumption that intervening experts in interactive imitation learning should be near-optimal and enables the algorithm to learn behaviors that improve over the potential suboptimal human expert.
arXiv Detail & Related papers (2023-11-21T21:05:21Z)
Relation-aware Ensemble Learning for Knowledge Graph Embedding [68.94900786314666]
We propose to learn an ensemble by leveraging existing methods in a relation-aware manner. exploring these semantics using relation-aware ensemble leads to a much larger search space than general ensemble methods. We propose a divide-search-combine algorithm RelEns-DSC that searches the relation-wise ensemble weights independently.
arXiv Detail & Related papers (2023-10-13T07:40:12Z)
Implicit Offline Reinforcement Learning via Supervised Learning [83.8241505499762]
Offline Reinforcement Learning (RL) via Supervised Learning is a simple and effective way to learn robotic skills from a dataset collected by policies of different expertise levels. We show how implicit models can leverage return information and match or outperform explicit algorithms to acquire robotic skills from fixed datasets.
arXiv Detail & Related papers (2022-10-21T21:59:42Z)
Deep Apprenticeship Learning for Playing Games [0.0]
We explore the feasibility of designing a learning model based on expert behaviour for complex, multidimensional tasks. We propose a novel method for apprenticeship learning based on the previous research on supervised learning techniques in reinforcement learning. Our method is applied to video frames from Atari games in order to teach an artificial agent to play those games.
arXiv Detail & Related papers (2022-05-16T19:52:45Z)
Maximum Entropy Model-based Reinforcement Learning [0.0]
This work connects exploration techniques and model-based reinforcement learning. We have designed a novel exploration method that takes into account features of the model-based approach. We also demonstrate through experiments that our method significantly improves the performance of the model-based algorithm Dreamer.
arXiv Detail & Related papers (2021-12-02T13:07:29Z)
Evolving Reinforcement Learning Algorithms [186.62294652057062]
We propose a method for meta-learning reinforcement learning algorithms. The learned algorithms are domain-agnostic and can generalize to new environments not seen during training. We highlight two learned algorithms which obtain good generalization performance over other classical control tasks, gridworld type tasks, and Atari games.
arXiv Detail & Related papers (2021-01-08T18:55:07Z)

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