reinforcement learning

The Urooj Arshad AI-Driven Autonomous Nano-Electroceutical Framework [UA-ANEF]: Overcoming the In Vivo Delivery Gap in Dormant Cancer Stem Cell Therapeutics

This theoretical blueprint introduces the Urooj Arshad AI-Driven Autonomous Nano-Electroceutical Framework [UA-ANEF-2026]. The model critiques current AI oncology systems for ignoring in vivo hydrodynamic drag—the "In Vivo Delivery Gap"—which leads to premature hepatic clearance of nanoparticles. We propose using 100 n...

SLO-Aware Data Replication and Consistency in the Computing Continuum: A Two-Stage MARL Approach

Smart cities rely on Internet of Things (IoT) sensing and Edge-to-Cloud computing infrastructures to deliver intelligent urban services with heterogeneous requirements. Safetycritical control loops require low latency and high availability, while planning and analytics workloads tolerate relaxed constraints. A single d...

Action-Space-Oriented Reinforcement Learning Compensation for PI-TPS-Controlled Low-Voltage DAB Converters Under Input-Voltage and Load Variations

This study investigates how the insertion point and parameterization of reinforcement-learning (RL) compensation affect a low-voltage dual-active-bridge (DAB) converter operated with triple-phase-shift (TPS) modulation. A proportional–integral (PI)-TPS controller is used as the common baseline. Two deep deterministic p...

Select-to-Act: Hierarchical Reinforcement Learning via Adaptive Language Guidance

Coordinated winter thermal management of fuel cell vehicles based on global-observation dual-agent reinforcement learning

Adaptive Biohybrid Neural Interface (ABNI): A Computational Feasibility Framework for Closed-Loop Neuromodulation, Thermal Safety, and System-Level Engineering Sub-Models

This paper presents the Adaptive Biohybrid Neural Interface (ABNI) as a computational feasibility framework: a set of independently verifiable mathematical and computational sub-models for the major subsystems such an interface would require, rather than an integrated, biologically validated device. Its core contributi...

Real-time reinforcement learning for turbulent state-dependent control in a bluff-body wake

Abstract Controlling turbulent dynamics remains a major challenge because their chaotic, multiscale nature strongly affects many fluid systems. Here, we present REACT, an autonomous reinforcement learning framework for real-time, state-dependent control of turbulent wake dynamics in a wind tunnel. Deployed on an Ahmed-...

Deep Diffusion Reinforcement Learning for Options Hedging

Diagnosing Faults in Reinforcement Learning Simulators and World Models with Canonical Polynomial Invariants

A large literature builds physical structure into learned dynamics on the premise that models respecting the underlying physics predict better. This work tests that premise using exact polynomial invariants recovered from trajectories and canonicalised as reduced Gröbner bases over ℚ. On Acrobot, exactness provides lit...

Diagnosing Faults in Reinforcement Learning Simulators and World Models with Canonical Polynomial Invariants

A large literature builds physical structure into learned dynamics on the premise that models respecting the underlying physics predict better. This work tests that premise using exact polynomial invariants recovered from trajectories and canonicalised as reduced Gröbner bases over ℚ. On Acrobot, exactness provides lit...

Fine-tuning small reasoning models for quantum field theory

Abstract Despite the growing application of large language models (LLMs) to theoretical physics, there has been little academic exploration of how domain-specific physics reasoning ability develops during training. To investigate this, we perform the first academic comparison of reinforcement learning and supervised fi...

A Survey on Reinforcement Learning Optimization Methods for Multi-Agent Collaboration of Large Language Models

The integration of reinforcement learning (RL) into the optimization of multi-agent collaboration for Large Language Models (LLMs) is an important combination of two advanced areas, Multi-Agent Systems (MAS) and LLMs. This paper thoroughly examines the main approaches, evaluation standards, recent progress and existing...