New Framework For Safe Reinforcement Learning Agents
Topics
Developing
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Sources · 7 independent
“Be Fair! Can Machine Learning Engineering Agents Adhere to Fairness Constraints?. Authors: Anna Richter, Julia Stoyanovich, Sebastian Schelter Abstract: Machine learning engineering (MLE) agents promise to automate end-to-end ML pipeline development from raw data and natural langu...”
“Provably Auditable and Safe LLM Agents from Human-Authored Ontologies. Authors: Aaron Sterling Abstract: We introduce the LLM agent architecture Agentic Redux, intended for use with nontrivial problem domains that require linear auditability. Using the typed lambda calc...”
“Learning While Acting: A Skill-Enhanced Test-Time Co-Evolution Framework for Online Lifelong Learning Agents. Authors: Bo Mao, Jie Zhou, Yutao Yang, Xin Li, Xian Wei, Qin Chen, Xingjiao Wu, Liang He Abstract: Lifelong learning is essential for Large Language Model (LLM) agents operating in dynamic, interacti...”
“Scenario Generation for Risk-Aware Reinforcement Learning with Probably Approximately Safe Guarantees. Guaranteeing safety is critical to the deployment of reinforcement learning (RL) agents in the real-world”
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