reinforcement learning

Mask-guided stereo vision technique for crack depth estimation of concrete bridge deck

Durability of reinforced concrete structures depends on early detection and accurate assessment of damage that accelerates reinforcement corrosion and long term deterioration. Cracks on bridge decks allow moisture and chloride ingress, making crack depth an important indicator of deterioration severity that cannot be c...

Towards Robust Deep Reinforcement Learning against Environmental State Perturbation

Abstract Adversarial attacks and robustness in Deep Reinforcement Learning (DRL) have been widely studied in various threat models; however, few consider environmental state perturbations, which are natural in embodied scenarios. To improve the robustness of DRL agents, we formulate the problem of environmental state p...

Agentic AI for Computational Fluid Dynamics: A Review

Computational fluid dynamics (CFD) workflows remain strongly dependent on expert decisions in geometry preparation, meshing, model and solver configuration, convergence assessment, post-processing, and design iteration. This review examines agentic artificial intelligence (AI) for CFD automation and engineering design,...

Intelligent Circuit Design: Exploring Artificial Intelligence for Logic Gate Synthesis and Optimization

Modern integrated circuits contain billions of logic gates, while thermal and sub-micron parasitic limits make every design decision more expensive. Classical logic synthesis relies on deterministic minimization and hand-engineered heuristics that struggle to balance Power, Performance, and Area (PPA) at the same time....

Self-Reflection and Error-Correction Mechanisms in LLMs Driven by Reinforcement Learning

Large language models have shown very strong abilities on complex reason work, but they still have many problems, these problems include error accumulation and hallucinations. Therefore, letting these models have the ability of self-reflection and error correction has hence become a core research focus. This research p...

Reinforcement learning for adaptive composition of quantum circuit optimisation passes

Abstract Many quantum software development kits provide a suite of circuit optimisation passes. These passes have been highly optimised and tested in isolation. However, the order in which they are applied is left to the user, or else defined in general-purpose default pass sequences. While general-purpose sequences mi...

RolloutPipe: Overlapping Pipelined Rollout and Training in Disaggregated On-Policy LLM Reinforcement Learning

Plug and schedule: scalable and modular architecture for integrated and dynamic scheduling based on graph neural networks and reinforcement learning

Terahertz high-quality-factor topological corner-state cavity designed via policy-based reinforcement learning

Abstract A single-diagonal breathing square-lattice second-order photonic topological insulator is studied, in which bulk-polarization calculations reveal that tuning the position of a single-diagonal pair of the ceramic rods within the unit cell can induce a topological phase transition. Through numerical analysis of...

A fast risk assessment method for power balance under extreme weather conditions based on deep reinforcement learning with a coupled attention mechanism

Even if persistent stagnant weather does not directly damage power grid equipment, it may lead to prolonged insufficient output of renewable energy sources and further trigger power balance risks. This paper proposes a rapid risk assessment framework that comprehensively incorporates meteorological sensitivity models,...

Simulation data and code for energy-neutral evaluation and compact reinforcement learning control of heterogeneous islanded microgrids

Preserved simulation and analysis evidence supporting the associated Energy manuscript. Includes 420 distinct saved evaluation traces across main, carbon/attention, low-SOC and compact-policy evaluations; completion flags for every attempt; selected actor checkpoints; original training histories; processed numerical ev...

Intelligent Navigation System Design for Underwater Robot Using Reinforcement Learning