reinforcement learning

BeamNG Research SDK (BRSDK)

BRSDK is a deterministic, high-frequency telemetry framework and Python SDK designed to bridge the Simulation-to-Real (Sim2Real) transfer gap in autonomous vehicle engineering, reinforcement learning, and vehicle dynamics within BeamNG.drive and BeamNG.tech.

Risk-aware eVTOL path planning for urban fixed-altitude cruise: A multimodal fusion reinforcement learning approach

The complexity and uncertainty of urban environments create an urgent need for safety-enhanced path planning in the autonomous navigation of electric vertical take-off and landing (eVTOL) aircraft. However, existing methods often focus solely on binary collision outcomes or suffer from local optima entrapment, failing...

Bilevel Traffic Flow Control Model for Highway Network Based on Deep Reinforcement Learning Under Emergency Events

A Review of Robot Adaptive Control Driven by Deep Reinforcement Learning

This review aims to clarify the development path of DRL-driven robot adaptive control, offering reference for theory, algorithm design, and engineering practice and it is hoped that this survey will help new researchers quickly grasp the landscape of the field.

Interpretable and Robust Car-Following Planning via Causality-Driven Deep Reinforcement Learning

CoT-RL-HAR: Chain-of-Thought Reinforcement Learning for Few-Shot Time-Series Human Activity Recognition

Predicting the Power Impact of Scheduling Decisions in Kubernetes Using Fine-Grained Monitoring and XGBoost

The rapid growth of cloud data centres has increased their energy consumption and environmental footprint, highlighting the need for more energy-efficient resource management. Kubernetes has become a widely adopted container orchestration platform for automating the deployment, scaling, and management of containerized...

Structured Episodic Memory (SEM)

Structured Episodic Memory (SEM) is an experimental non-neural agent architecture that combines overlapping state abstractions, associative value memories, eligibility-based temporal credit assignment, goal-conditioned contact control, and context-dependent strategy memory. We evaluate SEM in Pong using structured stat...

Reinforcement learning with control-barrier-function safety filters for trajectory tracking on a Stewart platform: campaign v3

Code, aggregated data and provenance for the simulation campaign behind the manuscript. Twelve training seeds, twenty-five evaluation episodes per seed on a fixed shared evaluation base, four arms. Raw policy checkpoints and TensorBoard logs are not included: they exceed two gigabytes and every reported number is deriv...

BeamNG Research SDK (BRSDK)

BRSDK is a deterministic, high-frequency telemetry framework and Python SDK designed to bridge the Simulation-to-Real (Sim2Real) transfer gap in autonomous vehicle engineering, reinforcement learning, and vehicle dynamics within BeamNG.drive and BeamNG.tech.

MulTADrug-GPT: A sample-efficient generative framework for multi-target drug design via reinforcement and preference learning

This repository contains the code, data, and pre-trained models for "MulTADrug-GPT: A sample-efficient generative framework for multi-target drug design via reinforcement and preference learning". For detailed instructions on environment setup and the step-by-step execution of the training and evaluation pipelines, ple...

Development of an Educational Platform for Reinforcement Learning Control of a Rotary Inverted Pendulum Based on LW-RCP

This study proposes a compact rotary inverted pendulum experimental platform designed for implementing reinforcement learning (RL) based controllers on physical systems. While RL demonstrates superior performance in simulated environments, applying it to real-world systems often leads to performance degradation and ins...