IEEE

Consensus-based Distributed Reinforcement Learning with Primal-Dual Update for Networked Microgrids On-Line Coordination

Abstract: This paper develops a distributed reinforcement learning (RL) method to coordinate cooperative microgrids (MGs). The high uncertainty of power loads and renewable energy sources motivate the ...
EurekAlert!

Reinforcement learning and blockchain: new strategies to secure the Internet of Medical Things

(A) Internet of Medical Things (IoMT) devices collect medical data then encrypt it and sent to a blockchain for secure storage. (B) Reinforcement learning (RL) agents monitor activity to detect ...
Wired

This Startup Wants to Spark a US DeepSeek Moment

Ever since DeepSeek burst onto the scene in January, momentum has grown around open source Chinese artificial intelligence models. Some researchers are pushing for an even more open approach to ...
marktechpost

RA3: Mid-Training with Temporal Action Abstractions for Faster Reinforcement Learning (RL) Post-Training in Code LLMs

TL;DR: A new research from Apple, formalizes what “mid-training” should do before reinforcement learning RL post-training and introduces RA3 (Reasoning as Action Abstractions)—an EM-style procedure ...
GitHub

CI test linux://rllib:learning_tests_multi_agent_footsies_ppo_gpu is flaky

Labels bug ci-test flaky-tracker ray-test-bot rllib stability triage weekly-release-blocker ...
Psychology Today

DeepMind on the Brain’s Dopamine System and AI

Artificial intelligence (AI) researchers strive to advance machine intelligence by applying theories and concepts of human intelligence for learning, motivation, memory, reasoning, and more. There are ...
GitHub

[RLlib][Unity] unity3d_env_local.py 'NoneType' for action spaces

I followed the ray tutorial based on this website: https://medium.com/distributed-computing-with-ray/reinforcement-learning-with-rllib-in-the-unity-game-engine ...
marktechpost

Meta Introduces LlamaRL: A Scalable PyTorch-Based Reinforcement Learning RL Framework for Efficient LLM Training at Scale

Reinforcement learning has emerged as a powerful approach to fine-tune large language models (LLMs) for more intelligent behavior. These models are already capable of performing a wide range of tasks, ...