By categorizing and filtering user input, you can better focus on driving AI improvement. This iterative process—blending automation with human review—ensures AI learns from high-quality data, leading ...

This important study presents single-unit activity collected during model-based (MB) and model-free (MF) reinforcement learning in non-human primates. The dataset was carefully collected, and the ...

Reward models holding back AI? DeepSeek's SPCT creates self-guiding critiques, promising more scalable intelligence for enterprise LLMs.

The review introduces a proposed two-layer reinforcement learning framework for distributed smart grid control. In this ...

Turing’s ideas ultimately led to the development of reinforcement learning, a branch of artificial intelligence. Reinforcement learning designs intelligent agents by training them to maximize rewards ...

Huang and colleagues examined neural responses in mouse anterior cingulate cortex (ACC) during a discrimination-avoidance task. The authors present useful findings that ACC neurons encode primarily ...

Our approach integrates a reinforcement learning-based path planning algorithm to guide the multi-robot formation in identifying diffusion sources, with a clustering-based method for destination ...

New approach flips the script on enterprise AI adoption by using input data you already have for fine-tuning instead of needing labelled data.