Reproduction package for the WAIMLAp 2026 paper A CVaR Safety Floor for Offline Reinforcement Learning under Unobserved Confounding: Duality and a Finite-Sample Validity Regime. It ships the confounded medical benchmark and its offline-data generator, the agents (CVaR-TRPO with the Safety-Floor acceptance gate, a CQL-s...
Jordan King Rodriguez Mallqui, Helder Rojas· Zenodo (CERN European Organi...· 0 citations
The State Extraction Graph (SEG) is proposed, which provides a significantly more concise structural representation than Petri nets for modeling multi-wafer fabrication processes and a Graph Deep Reinforcement Learning (G-DRL) method for scheduling cluster tools.
Bi-Feng Zhu, Bin Li, Jing-Hui Zhong· International Journal of Mac...· 0 citations
Project TALOS is an autonomous research intelligence platform powered by deep reinforcement learning (DDDQN), multi-tier LLM orchestration, and the Grey Wolf Optimizer (GWO). It conducts end-to-end scientific literature discovery and evaluation across 16 academic APIs.
Christos Smarlamakis, Efstratios Georgopoulos· Zenodo (CERN European Organi...· 0 citations
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Reproducibility package accompanying the manuscript "Dynamic service composition reconfiguration in cloud manufacturing under limited shared recovery capacity: A reinforcement learning approach".
saberrrostami· Zenodo (CERN European Organi...· 0 citations
Artifacts accompanying the paper “Invited: EDA-env: A Reinforcement Learning Environment for RTL-to-GDS Design with Independent Scoring.” See the included README for contents and reproduction instructions.
Amur Ghose, Andrew B. Kahng, Yugesh Kothari et al.· Zenodo (CERN European Organi...· 0 citations
Abstract EN This document, produced with the assistance of ChatGPT o3 and ChatGPT 5 Thinking, is released under the Apache 2.0 licence. It is a voluntary defensive publication (prior art) and therefore enters the prior art upon release under the applicable patent statutes EPC Art. 54(2) (European Patent Convention), Fr...
Pillet Xavier· Zenodo (CERN European Organi...· 0 citations
Artifacts accompanying the paper “Invited: EDA-env: A Reinforcement Learning Environment for RTL-to-GDS Design with Independent Scoring.” See the included README for contents and reproduction instructions.
Amur Ghose, Andrew B. Kahng, Yugesh Kothari et al.· Zenodo (CERN European Organi...· 0 citations
Developing a smart control system capable of accounting for the dynamics of industrial equipment state changes and promptly detecting features of developing failures is currently a pressing issue. The goal of this study is to develop an intelligent technology for industrial equipment state diagnostics based on a modifi...
G. Samigulina, Z. Samigulina, B. Dyussenkulova et al.· Discover Artificial Intellig...· 0 citations
Abstract ENThis document, produced with the assistance of ChatGPT o3 and GPT-5 Thinking, is released under the Apache 2.0 licence. It is a voluntary defensive publication (prior art) and therefore enters the prior art upon release under the applicable patent statutes (art. L 611-11 CPI / art. 54(2) CBE), 35 U.S.C. §102...
Xavier Pillet· Zenodo (CERN European Organi...· 0 citations
This record contains the replication package for the survey “The State of RL4SE: A Task-Oriented Review of Research on Reinforcement Learning for Software Engineering.” The package supports the study-identification, screening, classification, and artifact-availability results reported in the survey. It contains: • cand...
Yujia Fan· Zenodo (CERN European Organi...· 0 citations
We propose the Large Organismic Model (LOM), a developmental architecture for artificial intelligence. Rather than reporting the performance of an implemented system, this position paper formulates a testable computational hypothesis that integrates ideas from biology, developmental neuroscience, reinforcement learning...
T Hajime, T Masaki· Zenodo (CERN European Organi...· 0 citations
Students in MIT’s Concourse program delve deeply into the human condition, debate challenging questions, and learn to develop judgment about issues that can’t be quantified.
Training AI agents with reinforcement learning can be challenging because their tools, context, and decision-making are managed by complex frameworks. Agent Lightning connects existing agents to RL training, making it easier to improve them without rebuilding them. The post Agent Lightning v1.0: A 3,500-Line Lightweight Agentic RL Framework for Training Agents with Real Harnesses appeared first on Microsoft Research.
MIT News · Artificial Intelligence· news.mit.eduOct 6, 2026
