HeteroRL is a novel heterogeneous reinforcement learning framework designed for stable and scalable training of large language models (LLMs) in geographically distributed, resource-heterogeneous ...

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.

Reinforcement learning (RL) is machine learning (ML) in which the learning system adjusts its behavior to maximize the amount of reward and minimize the amount of punishment it receives over time ...

Download PDF Join the Discussion View in the ACM Digital Library Deep reinforcement learning (DRL) has elevated RL to complex environments by employing neural network representations of policies. 1 It ...

Abstract: This paper presents a simulation-based benchmarking analysis of three reinforcement learning (RL) algorithms—Soft Actor-Critic (SAC), Deep Q-Network (DQN), and Proximal Policy Optimization ...

This GitHub repository contains the code, data, and figures for the paper FedRAIN-Lite: Federated Reinforcement Algorithms for Improving Idealised Numerical Weather and Climate Models. Also includes ...

NVIDIA introduces NeMo-RL, an open-source library for reinforcement learning, enabling scalable training with GRPO and integration with Hugging Face models. NVIDIA has unveiled NeMo-RL, a cutting-edge ...