reinforcement learning

Deep reinforcement learning-based optimal energy management for microgrids

Experimental results demonstrate that the proposed DRL-based framework provides an effective and scalable solution for intelligent microgrid energy management and outperforms conventional rule-based strategies and model-based optimization approaches in terms of operational cost reduction, energy utilization efficiency,...

A closed-loop automated control architecture with physics-informed compensation for springback regulation in aerospace tube bending manufacturing

Springback in aerospace tube bending is a closed-loop control problem; the elastic rebound of the workpiece after tool release represents a systematic output error caused by material nonlinearities and batch-to-batch parametric uncertainties through a conventional fragmented workflow that lacks any feedback path for se...

Method of integrating deep reinforcement learning for electricity price prediction and intelligent trading strategy construction in smart grids

An intelligent trading strategy model based on Deep Deterministic Policy Gradient (DDPG) on the basis of the prediction model is constructed, providing optimal trading strategies for both electricity generation and consumption parties in the market.

Explainability-driven LangChain-integrated large language models and knowledge graphs for multiagent reinforcement learning in complex air combat simulation

This work advances multiagent reinforcement learning (MARL) for complex air combat by integrating methods that enhance decision-making and explainability. Using a realistic six-degrees-of-freedom aerial simulation built on the OpenAI Gymnasium framework, we investigate competitive agent interactions in dynamic scenario...

Interference modeling and adaptive suppression algorithm for HPLC and 920MHz wireless fusion communication in complex building environments

A composite interference recognition model based on short-time Fourier transform (STFT) and deep learning and a fast anti-interference algorithm based on transfer reinforcement learning (TrRL) is proposed, combining multi-agent Q-learning and value function transfer mechanism, adaptive spectrum access and power control...

Evaluating Deep Actor–Critic Methods for Path Planning of Mobile Manipulators Under Wheel–Terrain Interaction

The proposed framework incorporates a slip-aware reward formulation that penalizes discrepancies between commanded and measured robot motion while accounting for longitudinal and lateral slip resulting from wheel–terrain interaction, demonstrating the potential of slip-aware RL for coordinated SSMM navigation.

Human-robot collaboration disassembly sequence planning for end-of-life smartphones using Q-learning algorithm

Beyond Hand Skills: A Case Analysis of Empathy and Critical Thinking Instruction

Background In addition to scientific knowledge and technical proficiency, the compassionate care model of oral health care requires clinicians to develop and reliably demonstrate the skills of empathy and critical thinking. The benefits of these skills have been well documented and are accrued by both patients and clin...

Load frequency control framework of renewable integrated power systems in multi scenario disturbance conditions with the proximal policy optimization

Abstract The rapid adoption of renewable energy sources has radically changed the dynamic qualities of the modern power system, making load frequency control (LFC) problem worse due to unforeseeable generation, reduced system inertia, and the constant changes in disturbances. This paper presents a control framework bas...

Word-Class and Construction-Like Structure Emerges in Neural Successor Representations Trained on Natural Language

Language models are typically trained to predict the next token in a sequence. Here, we explore an alternative predictive principle from reinforcement learning: Successor Representations (SRs), which model the expected discounted distribution of future states rather than the immediate next state. We transfer this frame...

LUPittore: software che impara da solo a dipingere come un umano, prima versione, verrà accelerato l'apprendimento

# LUPittore[cite: 2] ## Description[cite: 2] LUPittore is an autonomous, standalone Python desktop application for Windows 10 and Windows 11 combining raster canvas manipulation, multilayer digital compositing, procedural brush engines, and autonomous stroke-based rendering (SBR)[cite: 2]. The software is implemented e...

LUPittore: software che impara da solo a dipingere come un umano, prima versione, verrà accelerato l'apprendimento

# LUPittore[cite: 2] ## Description[cite: 2] LUPittore is an autonomous, standalone Python desktop application for Windows 10 and Windows 11 combining raster canvas manipulation, multilayer digital compositing, procedural brush engines, and autonomous stroke-based rendering (SBR)[cite: 2]. The software is implemented e...