GitHub

Train multi-step agents for real-world tasks using GRPO.

RULER (Relative Universal LLM-Elicited Rewards) eliminates the need for hand-crafted reward functions by using an LLM-as-judge to automatically score agent trajectories. Simply define your task in the ...
IEEE

Distributed Microservice Deployment for Satellite Edge Computing Networks: A Multi-Agent Deep Reinforcement Learning Approach

Abstract: Low earth orbit (LEO) satellite networks are promising to carry out edge computing and reduce the service latency in future 6 G networks. Meanwhile, the microservice architecture provides a ...
IEEE

Quantum Deep Reinforcement Learning for Robot Navigation Tasks

Abstract: We utilize hybrid quantum deep reinforcement learning to learn navigation tasks for a simple, wheeled robot in simulated environments of increasing complexity. For this, we train ...
MilitaryNews.com

Reinforcement learning is making a buzz in space

A (NRL) research team successfully conducted the first reinforcement learning (RL) control of a free-flyer in space on May 27 ...

We Finally Know How Much It Cost to Train China’s Astonishing DeepSeek Model

DeepSeek found that it could improve the reasoning and outputs of its model simply by incentivizing it to perform a trial-and ...
Tech Xplore

The AI model that teaches itself to think through problems, no humans required

Artificial intelligence is getting smarter every day, but it still has its limits. One of the biggest challenges has been ...