Tech Xplore on MSN

Reinforcement learning accelerates model-free training of optical AI systems

Optical computing has emerged as a powerful approach for high-speed and energy-efficient information processing. Diffractive ...

True agentic AI is years away - here's why and how we get there

Today's AI agents are a primitive approximation of what agents are meant to be. True agentic AI requires serious advances in reinforcement learning and complex memory.
EurekAlert!

A graph reinforcement learning framework for real-time distributed multi-robot task allocation

Effective task allocation has become a critical challenge for multi-robot systems operating in dynamic environments like search and rescue. Traditional methods, often based on static data and ...
VentureBeat

DeepMind releases Acme, a distributed framework for reinforcement learning algorithm development

VentureBeat presents: AI Unleashed - An exclusive executive event for enterprise data leaders. Network and learn with industry peers. Learn More DeepMind this week released Acme, a framework intended ...
EurekAlert!

A reinforcement learning framework for guiding the agent to perform exploration based on clustering

Comparison between clustering-based bonus rewards with novelty alone (η = 1.0) and clustering-based bonus rewards (η = 0.5). Here, the collected states (blue dots) are clustered into 5 clusters and ...
Tech Xplore on MSN

AI gets a private tutor for learning human preferences more accurately

No matter how much data they learn, why do artificial intelligence (AI) models often miss the mark on human intent? Conventional comparison learning, designed to help AI understand human preferences, ...
Computerworld

Meta’s SPICE framework pushes AI toward self-learning without human supervision

The new reinforcement learning system lets large language models challenge and improve themselves using real-world data instead of curated training sets. Meta researchers have unveiled a new ...