Dual Alignment Theory Redefines Cross-Domain Offline RL Transfer

The paper revisits cross-domain offline reinforcement learning, showing that aligning both dynamics and value of source data is essential for effective policy transfer, and introduces the DVDF framework that jointly filters source samples, achieving consistent performance gains across multiple robotic control benchmarks.

DVDFPolicy Optimizationcross-domain transfer

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Dual Alignment Theory Redefines Cross-Domain Offline RL Transfer

DataFunTalk

Aug 13, 2023 · Artificial Intelligence

Model Innovation Forum: Advances in Recommendation Systems and Dense Retrieval

The Model Innovation Forum brings together academic and industry experts to discuss cutting‑edge recommendation system models, including efficient dense retrieval, Baidu ranking architectures, offline reinforcement learning, and large‑model inspirations, offering attendees deep technical insights and practical applications.

Artificial IntelligenceModel Innovationdense retrieval

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Model Innovation Forum: Advances in Recommendation Systems and Dense Retrieval

Didi Tech

Jun 13, 2023 · Operations

Supply-Demand Dynamics and Regulation Techniques in Didi’s Ride-Hailing Platform

Didi balances ride‑hailing supply and demand by forecasting regional needs with time‑series and deep‑learning models, then optimally repositioning drivers through integer programming and refining policies via imitation and offline reinforcement learning, ultimately enhancing passenger experience and platform efficiency.

DidiRide Hailingforecasting

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Supply-Demand Dynamics and Regulation Techniques in Didi’s Ride-Hailing Platform

DataFunSummit

Mar 5, 2023 · Artificial Intelligence

Data‑Driven Decision Optimization: Challenges and Advances in Offline Reinforcement Learning

This article reviews the practical challenges of applying data‑driven decision optimization in real‑world systems, explains the fundamentals of offline reinforcement learning, discusses recent algorithmic innovations such as policy‑constraint methods and the DOGE framework, and presents industrial case studies including power‑plant control and mixed offline‑online RL approaches.

Industrial AIdata-driven decisionoffline reinforcement learning

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Data‑Driven Decision Optimization: Challenges and Advances in Offline Reinforcement Learning