When RL is paired with human oversight, teams can shape how systems learn, correct course when context changes, and ensure ...

From autonomous cars to video games, reinforcement learning (machine learning through interaction with environments) can have an important impact. That may feel especially true, for example, when ...

Every year, NeurIPS produces hundreds of impressive papers, and a handful that subtly reset how practitioners think about scaling, evaluation and system design. In 2025, the most consequential works ...

DART addresses two critical limitations in existing stock market prediction systems. First, most graph-based approaches rely on static knowledge graphs that fail to capture the dynamic nature of ...

Stephen Curry hoped the questioning postgame would trend more positively Monday night when the Golden State Warriors thumped the Orlando Magic. Without the help of Draymond Green. "It's unfortunate ...

Abstract: A differential dynamic programming (DDP)-based framework for inverse reinforcement learning (IRL) is introduced to recover the parameters in the cost function, system dynamics, and ...

Dive into DeepSeek R1 and explore GRPO, reinforcement learning, and supervised fine-tuning (SFT) in an easy-to-understand way. Perfect for AI enthusiasts and beginners looking to grasp these concepts.

Download PDF Join the Discussion View in the ACM Digital Library Deep reinforcement learning (DRL) has elevated RL to complex environments by employing neural network representations of policies. 1 It ...

A lightning-fast crash course on JavaScript, the world’s most popular programming language. From its 1995 origins as Mocha in Netscape to powering front-end apps, Node.js servers, mobile apps, and ...