Agentic reinforcement learning research is constant algorithm modification. New estimators, new pipeline stages, new rollout schemes. In mainstream frameworks each change threads through layers of ...

Real-world robotic manipulation in homes and factories demands reliability, efficiency, and robustness that approach or surpass skilled human operators. We present a real-world reinforcement learning ...

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Training an animal on a complex task is often a painstaking, incremental process. This is because conventional behavioral learning protocols focus on minimizing reward to maximize trials. Gong et al.

Negative reinforcement is a frequently misused term that diminishes its value as a powerful tool for behavior change. You may be puzzled by the claim that negative reinforcement is actually a good ...

Over the past few years, AI systems have become much better at discerning images, generating language, and performing tasks within physical and virtual environments. Yet they still fail in ways that ...

Google's TorchTPU aims to enhance TPU compatibility with PyTorch Google seeks to help AI developers reduce reliance on Nvidia's CUDA ecosystem TorchTPU initiative is part of Google's plan to attract ...

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 ...

Abstract: The rapid evolution of modern electric power distribution systems into complex networks of interconnected active devices, distributed generation (DG), and storage poses increasing ...