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A cacao rush drives ‘alarming’ deforestation in Liberia #Liberia

A cacao rush drives ‘alarming’ deforestation in Liberia
A rush to plant cacao in southeastern Liberia is destroying large tracts of rainforest. According to satellite data as well as interviews with local environmental advocates and government officials, the establishment of new plantations in regions that border Côte d’Ivoire is causing extensive deforestation and spurring conflicts over land. While some of the plantations are […]

A Bio-Inspired Minimal Model for Non-Stationary K-Armed Bandits
Krubeal Danieli, Mikkel Elle Lepper{\o}d
https://arxiv.org/abs/2509.22209 https://arxiv.org…

A Bio-Inspired Minimal Model for Non-Stationary K-Armed Bandits
While reinforcement learning algorithms have made significant progress in solving multi-armed bandit problems, they often lack biological plausibility in architecture and dynamics. Here, we propose a bio-inspired neural model based on interacting populations of rate neurons, drawing inspiration from the orbitofrontal cortex and anterior cingulate cortex. Our model reports robust performance across various stochastic bandit problems, matching the effectiveness of standard algorithms such as Thom…

🇺🇦 #NowPlaying on BBCRadio3's #ThroughTheNight
Richard Wagner, œlafur Kjartan Sigurðarson, Iceland Symphony Orchestra & Eva Ollikainen:
🎵 Was duftet doch der Flieder, from 'Die Meistersinger von Nürnberg'
#RichardWagner #ÓlafurKjartanSigurðarson #IcelandSymphonyOrchestra

RLGS: Reinforcement Learning-Based Adaptive Hyperparameter Tuning for Gaussian Splatting
Zhan Li, Huangying Zhan, Changyang Li, Qingan Yan, Yi Xu
https://arxiv.org/abs/2508.04078

RLGS: Reinforcement Learning-Based Adaptive Hyperparameter Tuning for Gaussian Splatting
Hyperparameter tuning in 3D Gaussian Splatting (3DGS) is a labor-intensive and expert-driven process, often resulting in inconsistent reconstructions and suboptimal results. We propose RLGS, a plug-and-play reinforcement learning framework for adaptive hyperparameter tuning in 3DGS through lightweight policy modules, dynamically adjusting critical hyperparameters such as learning rates and densification thresholds. The framework is model-agnostic and seamlessly integrates into existing 3DGS pip…

Risk-Bounded Multi-Agent Visual Navigation via Dynamic Budget Allocation
Viraj Parimi, Brian C. Williams
https://arxiv.org/abs/2509.08157 https://arxiv.org…

Risk-Bounded Multi-Agent Visual Navigation via Dynamic Budget Allocation
Safe navigation is essential for autonomous systems operating in hazardous environments, especially when multiple agents must coordinate using just visual inputs over extended time horizons. Traditional planning methods excel at solving long-horizon tasks but rely on predefined distance metrics, while safe Reinforcement Learning (RL) can learn complex behaviors using high-dimensional inputs yet struggles with multi-agent, goal-conditioned scenarios. Recent work combined these paradigms by lever…

From Actions to Kinesics: Extracting Human Psychological States through Bodily Movements
Cheyu Lin, Katherine A. Flanigan
https://arxiv.org/abs/2510.04844 https://

From Actions to Kinesics: Extracting Human Psychological States through Bodily Movements
Understanding the dynamic relationship between humans and the built environment is a key challenge in disciplines ranging from environmental psychology to reinforcement learning (RL). A central obstacle in modeling these interactions is the inability to capture human psychological states in a way that is both generalizable and privacy preserving. Traditional methods rely on theoretical models or questionnaires, which are limited in scope, static, and labor intensive. We present a kinesics recog…

Implicit Updates for Average-Reward Temporal Difference Learning
Hwanwoo Kim, Dongkyu Derek Cho, Eric Laber
https://arxiv.org/abs/2510.06149 https://arxiv.…

Implicit Updates for Average-Reward Temporal Difference Learning
Temporal difference (TD) learning is a cornerstone of reinforcement learning. In the average-reward setting, standard TD($λ$) is highly sensitive to the choice of step-size and thus requires careful tuning to maintain numerical stability. We introduce average-reward implicit TD($λ$), which employs an implicit fixed point update to provide data-adaptive stabilization while preserving the per iteration computational complexity of standard average-reward TD($λ$). In contrast to prior finite-tim…