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New PAC learning approach for stochastic games with private info

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a new approach to PAC learning in turn-based stochastic games (TBSGs) with reachability objectives. This work introduces a method that allows for decentralized learning, where players do not share the same learning algorithm, and learning with private information, which is not shared with the other player. The study also proposes a game-theoretic generalization of the Expected Conditional Distance (ECD) parameter to measure the expected time to reach a target set, establishing a polynomial-sample complexity bound. AI

IMPACT Introduces novel decentralized and private learning methods for complex game theory scenarios, potentially advancing reinforcement learning capabilities.

RANK_REASON This is a research paper published on arXiv detailing a new theoretical approach to PAC learning in a specific type of game.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New PAC learning approach for stochastic games with private info

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COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Ali Asadi, Krishnendu Chatterjee, Pavol Kebis ·

    PAC Learning in Turn-Based Stochastic Games with Reachability Objectives: A Decentralized Private Approach via Expected Conditional Distance

    arXiv:2607.14877v1 Announce Type: new Abstract: Reachability is the most fundamental logical objective, yet it is notoriously difficult to learn in reinforcement learning settings: even for Markov decision processes, PAC learning of reachability is impossible without additional a…

  2. arXiv cs.LG TIER_1 English(EN) · Pavol Kebis ·

    PAC Learning in Turn-Based Stochastic Games with Reachability Objectives: A Decentralized Private Approach via Expected Conditional Distance

    Reachability is the most fundamental logical objective, yet it is notoriously difficult to learn in reinforcement learning settings: even for Markov decision processes, PAC learning of reachability is impossible without additional assumptions. This difficulty also holds in turn-b…

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