Tootfinder

Private Astronautenmission zurück auf der Erde
Nach zwei Wochen an Bord der Internationalen Raumstation ISS sind vier Raumfahrer der Axiom-4-Crew zurück auf der Erde. Die Mission brachte viele Premieren.
https://www.…

Few-shot Vision-based Human Activity Recognition with MLLM-based Visual Reinforcement Learning
Wenqi Zheng, Yutaka Arakawa
https://arxiv.org/abs/2508.10371 https://

Few-shot Vision-based Human Activity Recognition with MLLM-based Visual Reinforcement Learning
Reinforcement learning in large reasoning models enables learning from feedback on their outputs, making it particularly valuable in scenarios where fine-tuning data is limited. However, its application in multi-modal human activity recognition (HAR) domains remains largely underexplored. Our work extends reinforcement learning to the human activity recognition domain with multimodal large language models. By incorporating visual reinforcement learning in the training process, the model's gener…

Variance Reduced Policy Gradient Method for Multi-Objective Reinforcement Learning
Davide Guidobene, Lorenzo Benedetti, Diego Arapovic
https://arxiv.org/abs/2508.10608 https://

Variance Reduced Policy Gradient Method for Multi-Objective Reinforcement Learning
Multi-Objective Reinforcement Learning (MORL) is a generalization of traditional Reinforcement Learning (RL) that aims to optimize multiple, often conflicting objectives simultaneously rather than focusing on a single reward. This approach is crucial in complex decision-making scenarios where agents must balance trade-offs between various goals, such as maximizing performance while minimizing costs. We consider the problem of MORL where the objectives are combined using a non-linear scalarizati…

Ecuador: Stop land grabbing and racial discrimination for palm oil! https://www.rainforest-rescue.org/petitions/1270/ecuador-stop-land-grabbing-and-racial-discrimination-for-palm-oil

#petition Ecuador: Stop land grabbing and racial discrimination for palm oil!
Communities in northern Ecuador are fighting back against land grabbing and deforestation by the palm oil industry. Yet instead of ensuring law and order, the Ecuadorian authorities are siding with the companies – the courts are working to break the resistance of the people by ordering them to pay damages for their protests.

Value Function Initialization for Knowledge Transfer and Jump-start in Deep Reinforcement Learning
Soumia Mehimeh
https://arxiv.org/abs/2508.09277 https://…

Value Function Initialization for Knowledge Transfer and Jump-start in Deep Reinforcement Learning
Value function initialization (VFI) is an effective way to achieve a jumpstart in reinforcement learning (RL) by leveraging value estimates from prior tasks. While this approach is well established in tabular settings, extending it to deep reinforcement learning (DRL) poses challenges due to the continuous nature of the state-action space, the noisy approximations of neural networks, and the impracticality of storing all past models for reuse. In this work, we address these challenges and intro…

On Sunday, October 5, 2025, my wife and I are participating in the Canadian Cancer Society's Run for the Cure. This national fundraiser brings together communities across the country who will run or walk in support of all Canadians impacted by breast cancer. We are walking in honour of my mother, my two sisters, and both our daughters - all of whom have been touched by this terrible disease.
Our family isn't special as 1 in 8 women will face a breast cancer diagnosis in their lifetime, It is the most commonly diagnosed cancer among Canadian women, and all of us likely know someone who has battled it in the past, or is fighting it today. I encourage all Canadians to donate in honour of someone they know or simply to help their communities fight breast cancer. This cause is particularly important to us, especially this year, as we support our daughters, and I hope all Canadians get involved.
#CanadianCancerSociety #RunForTheCure
https://support.cancer.ca/site/TR/RunfortheCure/RFTC_NW_even_?px=15178695&pg=personal&fr_id=30455

It should surprise no one that AI watermarking is not going to work.
#ai

AI Watermark Remover Defeats Top Techniques
UnMarker targets AI image watermarks with precision. Find out how it scrambles security and what it means for the future of AI images.

MASH: Cooperative-Heterogeneous Multi-Agent Reinforcement Learning for Single Humanoid Robot Locomotion
Qi Liu, Xiaopeng Zhang, Mingshan Tan, Shuaikang Ma, Jinliang Ding, Yanjie Li
https://arxiv.org/abs/2508.10423

MASH: Cooperative-Heterogeneous Multi-Agent Reinforcement Learning for Single Humanoid Robot Locomotion
This paper proposes a novel method to enhance locomotion for a single humanoid robot through cooperative-heterogeneous multi-agent deep reinforcement learning (MARL). While most existing methods typically employ single-agent reinforcement learning algorithms for a single humanoid robot or MARL algorithms for multi-robot system tasks, we propose a distinct paradigm: applying cooperative-heterogeneous MARL to optimize locomotion for a single humanoid robot. The proposed method, multi-agent reinfo…

Federated Multi-Agent Reinforcement Learning for Privacy-Preserving and Energy-Aware Resource Management in 6G Edge Networks
Francisco Javier Esono Nkulu Andong, Qi Min
https://arxiv.org/abs/2509.10163

Federated Multi-Agent Reinforcement Learning for Privacy-Preserving and Energy-Aware Resource Management in 6G Edge Networks
As sixth-generation (6G) networks move toward ultra-dense, intelligent edge environments, efficient resource management under stringent privacy, mobility, and energy constraints becomes critical. This paper introduces a novel Federated Multi-Agent Reinforcement Learning (Fed-MARL) framework that incorporates cross-layer orchestration of both the MAC layer and application layer for energy-efficient, privacy-preserving, and real-time resource management across heterogeneous edge devices. Each age…

Stabilizing Long-term Multi-turn Reinforcement Learning with Gated Rewards
Zetian Sun, Dongfang Li, Zhuoen Chen, Yuhuai Qin, Baotian Hu
https://arxiv.org/abs/2508.10548 https://…

Stabilizing Long-term Multi-turn Reinforcement Learning with Gated Rewards
Reward sparsity in long-horizon reinforcement learning (RL) tasks remains a significant challenge, while existing outcome-based reward shaping struggles to define meaningful immediate rewards without introducing bias or requiring explicit task decomposition. Alternatively, verification-based reward shaping uses stepwise critics, but misalignment between immediate rewards and long-term objectives can lead to reward hacking and suboptimal policies. In this work, we address this problem in the con…