Breaking the Multi‑Robot Barrier: Sequential World‑Model Decomposition (ICLR 2026)

SeqWM introduces a sequential causal decomposition of joint dynamics, allowing each robot to model its marginal contribution conditioned on prior agents, which simplifies world‑model learning, enables intent‑sharing planning via MPPI, and achieves superior performance in challenging simulation benchmarks and real‑robot tests.

MPPIReinforcement LearningSeqWM

0 likes · 7 min read

Breaking the Multi‑Robot Barrier: Sequential World‑Model Decomposition (ICLR 2026)

Alimama Tech

Jan 8, 2025 · Artificial Intelligence

Model-Based Reinforcement Learning Auto‑Bidding Algorithms for Online Advertising

The paper introduces a model‑based reinforcement‑learning auto‑bidding framework that learns a neural‑network environment model from real logs, generates confidence‑aware virtual data fused with real data, and employs the COMBO+MICRO stabilizer and a Lagrange‑dual method for ROI‑constrained bidding, delivering up to 6.8 % higher consumption, 5 % GMV growth and 3.7 % ROI improvement on Alibaba’s platform.

Reinforcement Learningauto-biddingbudget constrained bidding

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Model-Based Reinforcement Learning Auto‑Bidding Algorithms for Online Advertising

DataFunSummit

Jul 8, 2024 · Artificial Intelligence

World Models and Causal Inference in Reinforcement Learning: A Comprehensive Overview

This article reviews the role of world (mental) models and causal inference in reinforcement learning, covering their theoretical foundations, model‑based RL frameworks such as Dyna, sample‑efficiency challenges, causal structure learning, distribution correction, dynamics‑reward modeling, and experimental results that demonstrate performance gains across multiple tasks.

Reinforcement LearningWorld Modelscausal inference

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World Models and Causal Inference in Reinforcement Learning: A Comprehensive Overview

DataFunTalk

Jan 25, 2024 · Artificial Intelligence

World Models, Reinforcement Learning, and Causal Inference: A Comprehensive Overview

This article presents a detailed overview of world models and their role in reinforcement learning, explains how causal inference can enhance model-based RL, discusses sample efficiency challenges, and shares experimental findings and practical insights from recent research and industry applications.

AIReinforcement Learningcausal inference

0 likes · 22 min read

World Models, Reinforcement Learning, and Causal Inference: A Comprehensive Overview

DataFunTalk

Apr 17, 2023 · Artificial Intelligence

Speculation: GPT-5 May Adopt Model‑Based Deep Reinforcement Learning for Unlimited Self‑Improvement

The article argues that the next generation GPT is likely to employ model‑based deep reinforcement learning, turning the model into both a policy and a world model, which could enable rapid, data‑efficient self‑enhancement but also raise serious safety and societal risks.

AI SafetyGPT-5deep reinforcement learning

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Speculation: GPT-5 May Adopt Model‑Based Deep Reinforcement Learning for Unlimited Self‑Improvement

Code DAO

Apr 28, 2022 · Artificial Intelligence

Model-Based Reinforcement Learning from Raw Video: A Detailed Walkthrough

The article explains how to train robots to learn tasks directly from raw video using model-based reinforcement learning, covering POMDP formulation, CNN auto‑encoders, latent‑space representations, iLQR optimization, and a step‑by‑step pipeline with concrete examples and references.

CNN autoencoderPOMDPReinforcement Learning

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Model-Based Reinforcement Learning from Raw Video: A Detailed Walkthrough

Breaking the Multi‑Robot Barrier: Sequential World‑Model Decomposition (ICLR 2026)