When RL is paired with human oversight, teams can shape how systems learn, correct course when context changes, and ensure ...
Motivated by "A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem" by Jiang et. al. 2017 [1]. In this project: Implement three state-of-art continous deep ...
Abstract: In continuous casting and rolling (CCR) systems, precise billet cutting is critical for ensuring product dimensional accuracy and minimizing material waste. However, conventional rule-based ...
According to God of Prompt on Twitter, DeepMind has published groundbreaking research in Nature led by David Silver, introducing an AI meta-learning system capable of autonomously discovering entirely ...
How do you convert real agent traces into reinforcement learning RL transitions to improve policy LLMs without changing your existing agent stack? Microsoft AI team releases Agent Lightning to help ...
Researchers from Japan’s University of Tsukuba have developed a novel imbalance-aware control framework for photovoltaic battery storage systems (PV-BSS) that trade in day-ahead electricity markets ...
Abstract: Large-scale overlapping problems (LSOPs) pose significant challenges in optimization due to the intricate interactions among the subcomponents. Traditional decomposition-based cooperative co ...
Flow-GRPO (Flow-based Group Refined Policy Optimization) converts long-horizon, sparse-reward optimization into tractable single-turn updates: Benchmarks. The research team evaluates four task types: ...
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