reinforcement learning

RLVacDiffSim: Reinforcement learning-driven simulation of vacancy diffusion

Code used to train mean-first-passage-time/time-estimator models and run deep-reinforcement-learning-accelerated atomistic simulations of vacancy diffusion.

A trust-aware edge-assisted reinforcement learning framework for secure and low-latency vehicular communication

Abstract Vehicular Ad Hoc Networks enable real-time intelligent transportation services but remain affected by dynamic mobility, unstable routing, congestion, malicious vehicles, and delayed emergency communication. This paper proposes a Trust-Aware Edge-Assisted Reinforcement Learning framework, termed TEARL-VANET, fo...

Comment on egusphere-2026-4021

Abstract. The Arctic Weather Satellite (AWS), launched by the European Space Agency (ESA) in August 2024, has enabled the first-ever global ice cloud remote sensing using 325 GHz terahertz channels. Owing to its short wavelength, the 325 GHz frequency band is highly sensitive to scattering by the three frozen hydromete...

Co-Designing TWT Scheduling and RF Energy Harvesting for Sustainable Heterogeneous Wi-Fi: A Deep Reinforcement Learning Approach

MODEL PEMBELAJARAN HOMESCHOOLING BLENDED LEARNING BERBASIS DIFERENSIASI KONTEN DAN PROSES

Homeschooling requires learning that is flexible and responsive to differences in learners’ readiness, interests, and needs. This study aimed to describe the implementation of content- and process-differentiated blended learning at Homeschooling Candrawinata Bandung, including its planning, implementation, evaluation,...

Review of: "Solving Sokoban using Hierarchical Reinforcement Learning with Landmarks"

NeatRL: Readable Single-File Reinforcement Learning Implementations in PyTorch

NeatRL is a reinforcement learning library built around single-file, self-contained implementations of classic and modern deep RL algorithms: DQN, A2C, PPO, DDPG, TD3, SAC and more, with Gymnasium and Atari support and Weights & Biases experiment tracking. NeatRL source code is licensed under the MIT License.

Calibrated Decisions Are Not Calibrated Probabilities: An Exact-Target Audit of Jev and Three Open Decision Models

Decision models answer typed questions with probabilities instead of text, and their main selling point is that those probabilities can be trusted. TypeSafe says its Jev model, trained with an unpublished method called Reinforcement Learning for Calibrated Decisions (RLCD), returns "epistemically honest" probabilities....

Enhancing large language model navigation via two-stage fine-tuning and sparse-reward reinforcement learning

NeuroGuard: An Interpretable Cyber Defense Agent Through Reinforcement Learning

Beyond Heuristics: A Research Journal on AI-Driven Logic Gate Synthesis and PPA Optimization in Modern Electronic Design Automation

Beyond Heuristics is a 3-page research journal on how Artificial Intelligence is changing logic gate synthesis in Electronic Design Automation (EDA). Modern chips contain billions of gates, and traditional rule-based synthesis struggles to balance power, performance, and area. The journal reviews three AI approaches to...

Robust disturbance-aware actor–critic reinforcement learning for multi-DOF robotic manipulators

Building on recent insights that augmenting reinforcement‐learning policies with disturbance estimates improves robustness and sim-to-real transfer, this paper proposes a disturbance-aware actor–critic RL framework for high‐precision robotic manipulators. We derive the dynamics of manipulators ranging from two to six d...