Tech Times

Open-Source Coding Model Ornith-1.0 Writes Its Own Training Scaffold in Reinforcement Learning

Open-source agentic coding model Ornith-1.0, released today under the MIT license, uses a self-improving reinforcement ...
Aerospace and Mechanical Insider on MSN

AI reinforcement learning tackles fusion plasma instabilities

The DIII-D National Fusion Facility in San Diego, operated by General Atomics, houses the largest and most advanced magnetic ...
IEEE

RL-Routing: An SDN Routing Algorithm Based on Deep Reinforcement Learning

Abstract: Communication networks are difficult to model and predict because they have become very sophisticated and dynamic. We develop a reinforcement learning routing algorithm (RLRouting) to solve ...

AI Is Designing Radio Chips That Humans Couldn’t Even Imagine

SummaryRFIC design is a complex “dark art” that limits progress in wireless technologies like 5G, autonomous vehicles, and ...
GitHub

RL Dresden Algorithm Suite

This suite implements several model-free off-policy deep reinforcement learning algorithms for discrete and continuous action spaces in PyTorch. DQN Single Discrete Mnih et. al. 2015 Double DQN Single ...
AHA/ASA Journals

Perioperative Anemia and Patient Blood Management in Cardiac Surgery: A Scientific Statement From the American Heart Association

Perioperative anemia and red blood cell transfusions are important risk factors for morbidity and mortality in cardiac ...
Aerospace and Mechanical Insider on MSN

Reinforcement learning tames confined cylinder wakes

In fluid dynamics, the wake behind a cylinder can exhibit complex vortex shedding, a phenomenon that becomes even more ...
IEEE

A Deep Reinforcement Learning Based Motion Cueing Algorithm for Vehicle Driving Simulation

Abstract: Motion cueing algorithms (MCA) are used to control the movement of motion simulation platforms (MSP) to reproduce the motion perception of a real vehicle driver as accurately as possible ...
VentureBeat

Alibaba's Metis agent cuts redundant AI tool calls from 98% to 2% — and gets more accurate doing it

One of the key challenges of building effective AI agents is teaching them to choose between using external tools or relying on their internal knowledge. But large language models are often trained to ...
the-decoder

RL agents go from face-planting to parkour when researchers keep adding network layers

An RL agent, by contrast, often gets only sparse feedback about whether it reached a goal or not. CRL teaches the agent a simple skill: to tell whether a move looks like part of a path that really ...
Frontiers

A combined approach to lithology identification using reinforcement learning and transformer algorithms

Lithology identification plays a pivotal role in logging interpretation during drilling operations, directly influencing drilling decisions and efficiency. Conventional lithology identification ...
techxplore

AI teaches itself and outperforms human-designed algorithms

Like humans, artificial intelligence learns by trial and error, but traditionally, it requires humans to set the ball rolling by designing the algorithms and rules that govern the learning process.