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New game-agnostic value function method shows promise in tabletop games

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new game-agnostic method for training value functions, called JSON-Bag VF, which utilizes tokenized JSON descriptions of game trajectories. This approach, enhanced by random forest feature selection and game-stage-specific feature selection, was evaluated on six tabletop games. The results indicate that JSON-Bag VF, particularly when using the One-step-look-ahead (OSLA) algorithm, outperforms baseline OSLA agents in most tested games, with feature selection proving to be the most critical factor for performance. AI

IMPACT This research introduces a novel, game-agnostic approach to training AI agents, potentially improving their adaptability across various game environments.

RANK_REASON The cluster contains a research paper detailing a new method for training value functions in AI. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New game-agnostic value function method shows promise in tabletop games

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The cluster contains a research paper detailing a new method for training value functions in AI. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Dien Nguyen, Diego Perez-Liebana ·

    Game-Agnostic Value Functions through Automatic JSON Feature Extraction

    arXiv:2608.30056v1 Announce Type: new Abstract: JSON Bag-of-Tokens (JSON-Bag) is a recently proposed method to generically represent game trajectories by tokenizing their JSON descriptions. We introduce JSON-Bag VF, a game-agnostic approach to training value functions for game-pl…

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