reinforcement learning

Preference Optimization for Open-Weight Language Models: A Survey and Low-Resource RLHF Case Study

Open-weight language-model post-training combines preference learning, reinforcement learning, verifiable feedback, and increasingly, agentic training and on-policy distillation. Comparing these systems requires separating their optimization algorithms from their feedback sources, sampling procedures, and evaluation ta...

Robotic Garment Folding via VLA Policy: Engineering, Not Pure Math — E8 Intelligence Research

FINDING: The search results are video titles/abstracts, not actual solutions. No mathematical content is extractable. The only concrete item is an arXiv abstract on robotic garment folding (LeHome Challenge 2026), which is engineering, not pure mathematics. MATH: No equations, constants, or ratios are present in the pr...

Towards an Integrated Production Scheduling and Maintenance Planning Using Reinforcement Learning

An improved MADDPG-based framework for bidirectional safety management of construction robot interaction

Purpose Safety risks are particularly pronounced when multiple construction robots collaborate in complex construction environments. To enhance collaborative efficiency while ensuring operational safety, this study develops and evaluates, in a scaled simulation, a bidirectional safety management approach that balances...

A battery life prediction method based on graph neural network assisted by deep reinforcement learning

With the wide utilization of new energy batteries, the accurate prediction of battery life has become an urgent technical issue that needs to be addressed. Studies have developed battery life prediction models based on correlation analysis for feature extraction. This study introduces a DQN-based graph structure genera...

Spectral SU(3) Gauge Invariant on Four-Dimensional Euclidean Lattices: Wilson Loop Expectation Values, Area Law Confinement, and the Non-Perturbative Mass Gap

Complete lattice computation of Wilson loop expectation values under SU(3) gauge theory on a four-dimensional Euclidean lattice. Establishes color confinement via the area law and extracts the non-perturbative Yang-Mills mass gap of 77.71 MeV from the two-point glueball correlator spectral decomposition. ⚖️ PROPRIETARY...

Image-Based Reinforcement Learning for Robust Mode Hop Recovery in External-Cavity Diode Lasers

Overview This is the initial public release of the code associated with the paper "Image-Based Reinforcement Learning for Robust Mode Hop Recovery in External-Cavity Diode Lasers." The repository provides a simulation and reinforcement-learning framework for recovering an external-cavity diode laser from mode hops usin...

Dahal Jeet: game engine, agents and experimental record

First public release, accompanying the manuscript "Search-based agents outperform reinforcement learning in Dahal Jeet, a Nepali imperfect-information card game", under review at Scientific Reports. Contains the game engine, 26 agents spanning hand-written heuristics, metaheuristic tuning, PIMC and single- and multiple...

Integrated V2X-Enabled Multi-Agent Deep Reinforcement Learning for Coordinated Emergency Vehicle Pre-emption

Emergency Vehicle Preemption (EVP) can substantially reduce response times during urban emergencies, yet aggressive green-wave strategies often induce severe secondary congestion on competing approaches and prolong network recovery. This paper presents a communication-aware Multi-Agent Proximal Policy Optimization (MAP...

Adaptive Anchor-Guided Spherical-Neutrosophic Intelligence for Uncertainty-Aware Engineering Systems — Research Software and Reproducibility Package

This repository provides the research software, reproducibility materials, benchmark results, and figure-generation resources accompanying the study “Adaptive Anchor-Guided Spherical-Neutrosophic Intelligence for Uncertainty-Aware Engineering Systems.” The package implements and supports the proposed Adaptive Anchor-Gu...

MudGym

A reinforcement learning environment for MUD2.

RAVEN Abstraction Engine: Proof-Carrying Cognitive Order, Autonomous Abstraction-Level Genesis, and Certified Effective Depth

RAVEN Abstraction Engine v4.0.0 develops a formal theory of autonomous cognitive-order genesis: the process by which an intelligent system can determine that its current representational and computational hierarchy is insufficient, construct a genuinely new higher-order level, ground that level in prior executable cogn...