Tootfinder

Amazon Rainforest hits record carbon emissions from 2024 forest fires https://news.mongabay.com/short-article/2025/10/amazon-rainforest-hits-record-carbon-emissions-from-2024-forest-fires/

Amazon Rainforest hits record carbon emissions from 2024 forest fires
In 2024, the Amazon Rainforest underwent its most devastating forest fire season in more than two decades. According to a new study by the European Commission’s Joint Research Centre, the fire-driven forest degradation released an estimated 791 million metric tons of carbon dioxide in 2024, a sevenfold increase compared with the previous two years. The […]

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.

"Amazon Rainforest hits record carbon emissions from 2024 forest fires"
#AmazonRainforest #Trees #Environment

Amazon Rainforest hits record carbon emissions from 2024 forest fires
In 2024, the Amazon Rainforest underwent its most devastating forest fire season in more than two decades. According to a new study by the European Commission’s Joint Research Centre, the fire-driven forest degradation released an estimated 791 million metric tons of carbon dioxide in 2024, a sevenfold increase compared with the previous two years. The […]

Vier Raumfahrer von der ISS abgedockt
Rund eine Woche nach der Ankunft ihrer Ablöse-Crew haben sich vier Raumfahrer von der ISS auf den Weg zurück zur Erde gemacht.
https://www.heise.de/news/Vier-Raumfah…

Model-Based Lookahead Reinforcement Learning for in-hand manipulation
Alexandre Lopes, Catarina Barata, Plinio Moreno
https://arxiv.org/abs/2510.08884 https://

Model-Based Lookahead Reinforcement Learning for in-hand manipulation
In-Hand Manipulation, as many other dexterous tasks, remains a difficult challenge in robotics by combining complex dynamic systems with the capability to control and manoeuvre various objects using its actuators. This work presents the application of a previously developed hybrid Reinforcement Learning (RL) Framework to In-Hand Manipulation task, verifying that it is capable of improving the performance of the task. The model combines concepts of both Model-Free and Model-Based Reinforcement L…

Feasibility-Guided Fair Adaptive Offline Reinforcement Learning for Medicaid Care Management
Sanjay Basu, Sadiq Y. Patel, Parth Sheth, Bhairavi Muralidharan, Namrata Elamaran, Aakriti Kinra, Rajaie Batniji
https://arxiv.org/abs/2509.09655

Feasibility-Guided Fair Adaptive Offline Reinforcement Learning for Medicaid Care Management
We introduce Feasibility-Guided Fair Adaptive Reinforcement Learning (FG-FARL), an offline RL procedure that calibrates per-group safety thresholds to reduce harm while equalizing a chosen fairness target (coverage or harm) across protected subgroups. Using de-identified longitudinal trajectories from a Medicaid population health management program, we evaluate FG-FARL against behavior cloning (BC) and HACO (Hybrid Adaptive Conformal Offline RL; a global conformal safety baseline). We report of…

Reinforcement Learning in Vision: A Survey
Weijia Wu, Chen Gao, Joya Chen, Kevin Qinghong Lin, Qingwei Meng, Yiming Zhang, Yuke Qiu, Hong Zhou, Mike Zheng Shou
https://arxiv.org/abs/2508.08189

Reinforcement Learning in Vision: A Survey
Recent advances at the intersection of reinforcement learning (RL) and visual intelligence have enabled agents that not only perceive complex visual scenes but also reason, generate, and act within them. This survey offers a critical and up-to-date synthesis of the field. We first formalize visual RL problems and trace the evolution of policy-optimization strategies from RLHF to verifiable reward paradigms, and from Proximal Policy Optimization to Group Relative Policy Optimization. We then org…

ReconDreamer-RL: Enhancing Reinforcement Learning via Diffusion-based Scene Reconstruction
Chaojun Ni, Guosheng Zhao, Xiaofeng Wang, Zheng Zhu, Wenkang Qin, Xinze Chen, Guanghong Jia, Guan Huang, Wenjun Mei
https://arxiv.org/abs/2508.08170

ReconDreamer-RL: Enhancing Reinforcement Learning via Diffusion-based Scene Reconstruction
Reinforcement learning for training end-to-end autonomous driving models in closed-loop simulations is gaining growing attention. However, most simulation environments differ significantly from real-world conditions, creating a substantial simulation-to-reality (sim2real) gap. To bridge this gap, some approaches utilize scene reconstruction techniques to create photorealistic environments as a simulator. While this improves realistic sensor simulation, these methods are inherently constrained b…

Incentivizing Safer Actions in Policy Optimization for Constrained Reinforcement Learning
Somnath Hazra, Pallab Dasgupta, Soumyajit Dey
https://arxiv.org/abs/2509.09208 https://…

Incentivizing Safer Actions in Policy Optimization for Constrained Reinforcement Learning
Constrained Reinforcement Learning (RL) aims to maximize the return while adhering to predefined constraint limits, which represent domain-specific safety requirements. In continuous control settings, where learning agents govern system actions, balancing the trade-off between reward maximization and constraint satisfaction remains a significant challenge. Policy optimization methods often exhibit instability near constraint boundaries, resulting in suboptimal training performance. To address t…

Stackelberg Coupling of Online Representation Learning and Reinforcement Learning
Fernando Martinez, Tao Li, Yingdong Lu, Juntao Chen
https://arxiv.org/abs/2508.07452 https://…

Stackelberg Coupling of Online Representation Learning and Reinforcement Learning
Integrated, end-to-end learning of representations and policies remains a cornerstone of deep reinforcement learning (RL). However, to address the challenge of learning effective features from a sparse reward signal, recent trends have shifted towards adding complex auxiliary objectives or fully decoupling the two processes, often at the cost of increased design complexity. This work proposes an alternative to both decoupling and naive end-to-end learning, arguing that performance can be signif…