reinforcement learning

Arabic Paraphrase Detection Using Transformers, Reinforcement Learning, and Ensemble Models

A data-driven model for stable long-horizon autoregressive prediction of plasma current and control-oriented boundary evolution in EAST

Accurate long-horizon prediction of tokamak plasma current, position, and boundary evolution is essential for magnetic control, rapid controller development, and reinforcement-learning-based optimization. However, high-fidelity physics-based simulators are often computationally prohibitive when large numbers of simulat...

Machine Learning–Driven Seismic Vulnerability Assessment of Prefabricated Concrete Beam–Column Joints Using High-Fidelity Simulations

Amanollahi Geodesic Curvature Flow Solver

We introduce the official open-access repository of the Amanollahi Geodesic Curvature Flow Solver, a breakthrough continuous optimization paradigm for Boolean Satisfiability (SAT) that definitively bridges a 40-year theoretical gap in pure continuous constraint satisfaction. Historically, continuous optimization framew...

Codebase for Reinforcement Learning for Quantum Network Control with Application-Driven Objectives

A reinforcement learning (RL) framework written in C++ as a building block for quantum network control. The code implements the MDPs from the work titled "Reinforcement Learning for Quantum Network Control with Application-Driven Objectives," using policy gradients and the chain rule to optimize arbitrary non-linear ob...

Latency-efficient cloud-edge cultural pattern generation via lossless lightweight condition transmission and bandwidth-adaptive scheduling

Cloud-edge systems for controllable diffusion generation typically upload the raw reference image, although ControlNet consumes only a sparse structural condition derived from it. We present a cloud-edge framework for directed cultural pattern generation in which the edge extracts the condition map, compresses it lossl...

From Bellman to Real-Time: Extensions to Complex Weather Regimes, Physics-Informed Optimization, and Full-Scale Validation

In our earlier methodology paper, we introduced a hierarchical framework combining graph compression, Diffusion Convolutional Recurrent Neural Networks (DCRNNs), and Multi-Agent Reinforcement Learning (MARL) to approximate Bellman’s optimality principle for real-time energy system control, validated using Palm Springs,...

Space-Filling Input Design for Nonlinear System Identification: A Reinforcement Learning Approach

Classical input design minimises the covariance of the estimated model parameters. For nonlinear black-box models, however, the dominant error is the bias induced by the model structure over the region of interest, which motivates space-filling strategies seeking to cover the system state space as uniformly as possible...

A hierarchical reinforcement learning approach for robot navigation integrating LiDAR priors and the options framework

This method formalizes the navigation task as a semiMarkov decision process and constructs a two-layer decision architecture with collaboration between a high-level manager and a low-level worker with collaboration between a high-level manager and a low-level worker.

Evolutionary Theory Makes Predictions About Cognition Beyond Rational Optimization

Evolution by natural selection produces the appearance of design in minds, brains, and behavior. While rational models of cognition are usually considered to be consistent with evolution, they are rarely derived from distinctively evolutionary assumptions. Bayesian modeling, reinforcement learning, rational analysis, a...

Spectral Feature Extraction for Reinforcement Learning in Financial Markets Under Nonstationary Dynamics

Reinforcement learning (RL) offers a principled framework for adaptive trading under the nonstationary dynamics of financial markets, yet the interaction between state representation and reward design remains poorly understood. This study represents a controlled empirical comparison of six RL trading agents trained via...

zannunakiz/Q1research_DQN_AutonomousVehicles: DQN_AV V1.0.0

Safety-Aware DQN Variants for AV Collision Avoidance This release contains the research software and experiment code supporting the study "Safety-Aware Deep Q-Network Variants for Lightweight Sensor-Based Collision Avoidance in Autonomous Vehicles." The project compares three Deep Reinforcement Learning variants, namel...