reinforcement learning

Corporate AI Training Program Template

A template for corporate AI training with role groups, learning objectives, governance practice, session design and post-training reinforcement.

Robotic Garment Folding via VLA Policy: No Pure Math Content Found — E8 Intelligence Research

FINDING: The search results are video titles and abstracts, not actual mathematical solutions. No equations, constants, or derivations are present. The only concrete mathematical content is a reference to an arXiv paper on robotic garment folding (LeHome Challenge 2026), which is applied robotics, not pure competition...

Structured Prediction Meets Neural Geometry: How Compositionality, Representational Manifolds, Criticality, and Hierarchical Coding Jointly Constrain What Neural Circuits Compute

This version (2026-09-26) corrects a citation error found by an automated check and confirmed by hand against the arXiv abstracts. Version 2 cited the identifier 2606.04428, an unrelated astrophysics paper on fast-spinning black holes, for the theoretical framework in which chaotic recurrent dynamics produce local roug...

Residual-Gated Diffusion Augmentation for Multi-Agent Reinforcement Learning in Energy Management

Sub-second dopaminergic reinforcement orchestrates juvenile social play and is disrupted in Shank3 deficiency. Chen et al. (Enriched version)

This dataset accompanies a study testing whether nucleus accumbens dopamine (DA) acts as an action-contingent reinforcement signal shaping juvenile social play in a sex-divergent manner, and whether Shank3 deficiency disrupts this signal. We collected multi-modal data from wild‑type (WT) and Shank3+/- (PND 35-85) durin...

Marwa-Chouhdary/MEC_IIoT_E_DDPG_LS_MCOR_TD3_Code: MEC IIoT E-DDPG-LS and MCOR-TD3 Code v1.0.0

MCOR-TD3 for MEC-Enabled IIoT — v1.0.1 This release contains Python code associated with the paper "Adaptive Task Caching and Resource Allocation in MEC-Enabled IIoT Using Deep Reinforcement Learning." It supports simulation of cooperative task offloading and computing-resource allocation using MCOR-TD3 to minimize max...

Hierarchical graph reinforcement learning for coordinated dispatch and reconfiguration of active distribution networks

Traditional studies on active distribution networks often optimize operational dispatch and network topology reconfiguration in a decoupled manner, although the two decisions are strongly coupled through power-flow redistribution, controllable-device outputs, and network constraints. This paper develops a problem-speci...

The ‘stickiness’ of disgust: Testing the hypothesis of disgust intractability in the context of instrumental reversal learning.

Discrete emotion theories conceptualise fear and disgust as distinct emotions. This notion is supported by the finding that Pavlovian disgust conditioning is more resistant to extinction than fear conditioning—disgust may be ‘stickier’ than fear. Dimensional emotion theories challenge this, suggesting that categorical...

PIREUS: Autonomous Operator Foundry, Hardware Capability Ontology, and Verifiable Reinforcement Learning Compiler Framework for Sounio

PIREUS is an autonomous operator foundry, hardware-capability ontology engine, and verifiable reinforcement learning compiler framework designed for the Sounio programming language ecosystem. It operates at the intersection of non-associative Cayley-Dickson hypercomplex algebras, formal silicon microarchitecture ontolo...

Digital Twin-Based Simulation of Electric VehicleInteraction within Smart City Energy Networks

This work provides a comprehensive roadmap for developing intelligent, sustainable and resilient EV charging ecosystems that will resolve technical, economical, and ecologicalconsiderations while protecting the privacy of data and securing the system.

Residual-Gated Diffusion Augmentation for Multi-Agent Reinforcement Learning in Energy Management

Energy-Adaptive Online Continual Learning Framework with Dynamic Model Compression for Real-Time Sensor-Driven Embedded Systems