Policy Consensus-Based Distributed Deterministic Multi-Agent Reinforcement Learning

Engineering, Electrical Engineering, Information Science, Computer Science, Artificial Intelligence,
policy consensus   distributed algorithms   multi-agent reinforcement learning   deterministic policy gradient   directed graphs   continuous control   deep reinforcement learning  

Policy Consensus-Based Distributed Deterministic Multi-Agent Reinforcement Learning Research Report Reinforcement Learning (RL) has made significant breakthroughs in recent years in various fields such as robotics, smart grids, and autonomous driving. However, in real-world scenarios, multi-agent collaboration problems, also known as Multi-Agent Re...

An Improved and Explainable Electricity Price Forecasting Model via SHAP-Based Error Compensation Approach

Engineering, Energy Engineering, Information Science, Electronic Information, Artificial Intelligence,
electricity price forecasting   explainable artificial intelligence   SHAP method   error compensation   neural networks   electricity markets   price volatility  

Improved Electricity Price Forecasting Model Based on SHAP and Its Explainability Analysis Background and Research Motivation Electricity price forecasting (EPF) models have become a hot research topic in recent years, particularly due to the financial impact of market volatility on stakeholders. Especially in European energy markets, recent years ...

Multiobjective Dynamic Flexible Job Shop Scheduling with Biased Objectives via Multitask Genetic Programming

Engineering, Mechanical Engineering, Information Science, Computer Science, Automation, Artificial Intelligence,
dynamic scheduling   flexible job shop   genetic programming   multitask learning   multiobjective optimization   scheduling heuristics   algorithm efficiency  

Breakthrough Research in Multiobjective Dynamic Flexible Job Shop Scheduling: An Innovative Approach to Optimize Biased Objectives via Multitask Learning in Genetic Programming Background Introduction Dynamic Flexible Job Shop Scheduling (DFJSS) is an essential combinatorial optimization problem with extensive real-world applications in areas such ...

NPE-DRL: Enhancing Perception-Constrained Obstacle Avoidance with Nonexpert Policy-Guided Reinforcement Learning

Engineering, Aerospace Engineering, Information Science, Computer Science, Artificial Intelligence,
UAVs   Obstacle avoidance   Deep reinforcement learning   Nonexpert policy   Perception-constrained   Sample efficiency   Monocular vision  

Research on Improving UAV Obstacle Avoidance in Vision-Constrained Environments Based on Nonexpert Policy Reinforcement Learning In recent years, unmanned aerial vehicles (UAVs) have gained widespread application in civilian fields such as package delivery, risk assessment, and emergency rescue, owing to their superior maneuverability and versatili...

Efficient CORDIC-based Activation Function Implementations for RNN Acceleration on FPGAs

Engineering, Electrical Engineering, Information Science, Electronic Information, Computer Science, Artificial Intelligence,
CORDIC   RNN   activation function   FPGA   hardware acceleration   natural language processing   edge devices  

Efficient Implementation of RNN Activation Functions: Breakthroughs in CORDIC Algorithms and FPGA Hardware Acceleration Background and Research Significance In recent years, with the rapid advancement of deep learning technologies, Recurrent Neural Networks (RNNs), particularly Long Short-Term Memory (LSTM) networks, have demonstrated powerful capa...