reinforcement learning

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"

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

Reduced model-based control in gambling disorder despite seemingly intact neural value and task structure representations

Abstract Background Disordered gambling has been linked to impairments in goal-directed (model-based) control and reinforcement learning. Methods Here we investigated the potential neural basis of this impairment using a sequential reinforcement learning task (modified two-step-task), computational modeling, and functi...

Developing Strategic Communication Insights for Social Change through Dialogic Learning

Scholars, researchers, and practitioners are aware of the urgency to develop boundary-spanning concepts in strategic communication to create dialogues for social change. While literature is abundant about conducting research, collecting data, creating strategies, and conceptualising big ideas, the complex non-linear pr...

从控制到动态平衡:超级智能对齐的博弈论与热力学重构

Current research on AI alignment mainly follows paths such as reinforcement learning from human feedback, scalable oversight, and interpretability. Its implicit premise is that there exists a centralized designer capable of defining clear objectives and exercising effective control over the system. However, when AI sys...