The article discusses reinforcement learning (RL) in multi-agent systems (MAS), focusing on innate-values-driven behaviors of AI agents. It introduces a hierarchical compound intrinsic value reinforcement learning model, termed IVRL, to…
Continue readingThe paper presents ICE-GRT, a model developed to enhance the performance of Large Language Models (LLMs) in domain-specific tasks. LLMs like ChatGPT and LLaMA often lack depth and accuracy in…
Continue readingThe paper introduces a technique for online symbolic music alignment using reinforcement learning. The reinforcement learning agent, an attention-based neural network, is trained to iteratively predict the current score position…
Continue readingThe article discusses a novel learning-based multi-perspective visual servoing framework that uses reinforcement learning to estimate robot actions from latent space representations of visual states. The approach was tested in…
Continue readingThis study presents a vision and proprioceptive data-driven robot control model for automating anchor-bolt insertion in construction. The model is designed to be robust under variable conditions such as lighting…
Continue readingDiffusion Reward is a novel framework that learns rewards from expert videos for reinforcement learning tasks. The system leverages conditional video diffusion models to solve complex visual reinforcement learning problems….
Continue readingThis article by researchers from UC Berkeley and Google DeepMind explores leveraging reinforcement learning (RL) to enhance the capabilities of large language models (LLMs). The authors note that standard prompting…
Continue readingThe research article proposes a solution to integrate Visual Semantic Navigation (VSN) models into real robots. The authors have released a ROS-based framework for VSN, ROS4VSN, which can be used…
Continue readingThe study investigates whether incorporating human preferences in Reinforcement Learning (RL) can enhance the transparency of robot behaviours. A shielding mechanism is integrated into the RL algorithm to monitor the…
Continue readingThe article presents a new approach to controlling quadcopters for time-optimal flight using end-to-end reinforcement learning (E2E). Traditional quadcopter control methods face difficulties with unmodeled effects. Recent advancements have brought…
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