The Next Platform

Google Uses AI Reinforcement Learning For Quantum Error Correction

Nvidia scientists and their counterparts at a range of academic, scientific, and quantum computing institutions late last ...

Reinforcement Learning With Metacognitive Feedback Is Offered As A Next-Gen Way To Shape AI LLMs

New method to tune LLMs is RLMF, reinforcement learning with metacognitive feedback. It is akin to RLAIF and somewhat like ...
marktechpost

Implementing Deep Q-Learning (DQN) from Scratch Using RLax JAX Haiku and Optax to Train a CartPole Reinforcement Learning Agent

In this tutorial, we implement a reinforcement learning agent using RLax, a research-oriented library developed by Google DeepMind for building reinforcement learning algorithms with JAX. We combine ...
InfoQ

Autonomous Big Data Optimization: Multi-Agent Reinforcement Learning to Achieve Self-Tuning Apache Spark

A monthly overview of things you need to know as an architect or aspiring architect. Unlock the full InfoQ experience by logging in! Stay updated with your favorite authors and topics, engage with ...
acm.org

Specification-Guided Reinforcement Learning

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 ...
Frontiers

Deep reinforcement learning-aided autonomous navigation with landmark generators

Mobile robots are playing an increasingly significant role in social life and industrial production, such as searching and rescuing robots, autonomous exploration of sweeping robots, and so on.
Frontiers

Reinforcement learning as an innovative model-based approach: Examples from precision dosing, digital health and computational psychiatry

Model-based approaches are instrumental for successful drug development and use. Anchored within pharmacological principles, through mathematical modeling they contribute to the quantification of drug ...