The ability to make adaptive decisions in uncertain environments is a fundamental characteristic of biological intelligence. Historically, computational ...

Abstract: Reinforcement Learning (RL) has emerged as a powerful paradigm for optimizing control systems by enabling autonomous decision-making in dynamic environments. This review provides a ...

Robust Reinforcement Learning-based model for UAV self-separation under Uncertainty. Hybrid; Amsterdam , Noord-Holland , Netherlands; Aerosp ...

All results from 3 seeds × 18 test instances = 54 evaluation points. BO static outperforms PPO on small instances, but PPO overtakes at 500-variable scale. learned-control-layers/ ├── src/ │ ├── ...

Many enterprise RAG pipelines handle one type of search well and fail silently on the rest. Databricks on March 4 released a new agent called KARL, or Knowledge Agents via Reinforcement Learning, that ...

ABSTRACT: Oracle-based quantum algorithms cannot use deep loops because quantum states exist only as mathematical amplitudes in Hilbert space with no physical substrate. Critically, quantum wave ...

Explore the reinforcement learning algorithm that achieves performance comparable to GRPO in RLVR with minimal complexity. Learn how it works, why it’s effective, and its practical applications in RL ...

In reinforcement learning (RL), an agent learns to achieve its goal by interacting with its environment and learning from feedback about its successes and failures. This feedback is typically encoded ...

Researchers at Google have developed a technique that makes it easier for AI models to learn complex reasoning tasks that usually cause LLMs to hallucinate or fall apart. Instead of training LLMs ...

Abstract: Autonomous Vehicles (AVs) rely extensively on GPS signals for navigation, exposing them to a wide range of GPS spoofing attacks, from simplistic signal manipulation to sophisticated, ...

Wind turbine control systems have evolved significantly over the past decades, moving from simple classical controllers to sophisticated artificial intelligence-based strategies. Early utility-scale ...

The path planning capability of autonomous robots in complex environments is crucial for their widespread application in the real world. However, long-term decision-making and sparse reward signals ...