Traditional RL uses single scalar rewards. RLAF uses multi-perspective critic ensembles: rlaf/ ├── agents/ # Actor and Critic agents │ ├── actor.py # Agent being trained │ └── critic.py # Evaluation ...

Despite holding onto a 5-0 record and being the No. 14 team in the country, the Missouri Tigers have plenty of areas to improve in. One of those areas, or players, is quarterback Beau Pribula. Though ...

Abstract: In the domain of continuous control, deep reinforcement learning (DRL) demonstrates promising results. However, the dependence of DRL on deep neural networks (DNNs) results in the demand for ...

Introduction: Optimizing the operation of interconnected hydropower systems presents significant challenges due to complex non-linear dynamics, hydrological uncertainty, and the need to balance ...

The age of truly autonomous artificial intelligence, where systems proactively learn, adapt and optimize amid real-world complexities instead of simply reacting, has been a long-held aspiration. Now, ...

Forbes contributors publish independent expert analyses and insights. Author, Researcher and Speaker on Technology and Business Innovation. Apr 19, 2025, 03:24am EDT Apr 21, 2025, 10:40am EDT ...

ABSTRACT: Offline reinforcement learning (RL) focuses on learning policies using static datasets without further exploration. With the introduction of distributional reinforcement learning into ...

1 Department of Mathematics, College of Sciences, Shanghai University, Shanghai, China. 2 School of Future Technology, and Department of Mathematics, College of Sciences, Shanghai University, Shanghai ...

Large language models (LLMs) are rapidly transforming into autonomous agents capable of performing complex tasks that require reasoning, decision-making, and adaptability. These agents are deployed in ...