Abstract: Optimal control of unknown nonlinear systems is challenging due to the absence of the underlying dynamics model. Reinforcement learning (RL) has become an effective framework for such ...

Rachel Reeves is scapegoating supermarkets for rising oil prices while ignoring algorithms that can learn ant-competitive ...

08/27/2025: Megatron-RL is actively under development. While it is functional internally at NVIDIA, it is not yet usable by external users because not all required code has been released. The ...

ABSTRACT: Oracle-based quantum algorithms cannot use deep loops because quantum states exist only as mathematical amplitudes in Hilbert space with no physical substrate. Critically, quantum wave ...

In reinforcement learning (RL), an agent learns to achieve its goal by interacting with its environment and learning from feedback about its successes and failures. This feedback is typically encoded ...

Researchers at Google have developed a technique that makes it easier for AI models to learn complex reasoning tasks that usually cause LLMs to hallucinate or fall apart. Instead of training LLMs ...

The path planning capability of autonomous robots in complex environments is crucial for their widespread application in the real world. However, long-term decision-making and sparse reward signals ...

AI agents are reshaping software development, from writing code to carrying out complex instructions. Yet LLM-based agents are prone to errors and often perform poorly on complicated, multi-step tasks ...