reinforcement learning

VLM-Mapper: Multimodal vision-language models for prior guided lane-level HD map construction

The automated construction of High-Definition (HD) maps from remote sensing data is essential for modern intelligent transportation systems and spatial data infrastructure. While imagery provides a scalable solution for lane-level HD map construction, traditional discriminative models often struggle in complex scenario...

Discovering session-level demand patterns for reinforcement learning-based dynamic inventory allocation in live-streaming e-commerce

Pre-control scheme generation for extreme-weather power imbalance risk using multi-resource frequency regulation and deep reinforcement learning

To address the power imbalance risk between renewable energy output and load demand under extreme weather conditions, this paper proposes a pre-control scheme generation method based on the integration of multiple frequency regulation resources and deep reinforcement learning. First, mechanism models for wind power and...

Machine Learning for Logic Gate Synthesis and Optimization in Electronic Design Automation

The increasing complexity of integrated circuits has made logic synthesis and gate-level optimization important bottlenecks in electronic design automation (EDA). Conventional synthesis flows rely on deterministic transformations, handcrafted heuristics, and repeated evaluation of large design spaces. Machine learning...

Exploration Cost: The Exploration Coefficient in Onboard Reinforcement-Learning Schedulers Is an Independent Driver of Battery Aging

Reinforcement-learning (RL) schedulers are now flying on real spacecraft: NASA’s Carruthers Geocorona Observatory (launched 24 September 2025, per NASA’s own mission status page — not itself a LEO mission; it orbits near the Sun–Earth L1 point) uses deep RL as its default operational scheduler for long-horizon operatio...

Rule-Guided Explainable Testing and Improvement of Deep Reinforcement Learning Policies

CORTEX: An Architecture for Persistent Cognitive Agents. With Verified Persistence, Typed Failure Semantics, and Bounded Cognition

CORTEX: An Architecture for Persistent Cognitive Agents. With Verified Persistence, Typed Failure Semantics, and Bounded Cognition Chloe J. Tully Independent Researcher Engineer https://orcid.org/0009-0007-5661-7332 https://doi.org/10.5281/zenodo.23183421 Tamworth NSW AUSTRALIA October 2026 --- Abstract Long-running co...

Machina Mirabilis (GPT-1900)

Machina Mirabilis (GPT-1900) investigates whether a language model trained from scratch on historical text can generate conceptually useful explanations of observations associated with later developments in physics. The project reports a 3.3-billion-parameter transformer and approximately 22 billion tokens of filtered...

AI-Based Optimization of Logic Gates for Improved Processor Performance

This literature-based review looks at how artificial intelligence, including machine learning and reinforcement learning, can help optimize logic gates and circuits in processor design. It explains how gates, ALUs, and processor performance connect, compares conventional logic synthesis with AI-assisted methods, and su...

Deep reinforcement learning applied to statistical arbitrage investment strategy on cryptomarket

Considerando el aumento al acceso a la información de mercado, en particular el libre acceso a información detallada sobre transacciones de cryptomonedas, junto con la compleja y dinámica propiedad de los mercados financieros, donde se requieren cada vez estrategias de inversión más sofisticadas, el aprendizaje reforza...

Review of: "Protein Structure Prediction in the 3D HP Model Using Deep Reinforcement Learning"

What the work claimsThe authors treat folding in the 3D Hydrophobic-Polar lattice model as a sequential decision problem: the chain is built as a self-avoiding walk on the cubic lattice, one residue per step, and a Deep Q-Network with experience replay and a target network learns where to place the next residue, with t...

Advanced Portfolio Optimization: Integrating Classical Frameworks with Machine Learning and Modern Asset Allocation