reinforcement learning

An adaptive unscented Kalman Filter with nonlinear model predictive control based on reinforcement learning for the induction motor control in electric vehicles

Mitigating perception-induced false safety in USV navigation: Memory- and perception-credibility-aware adaptive-risk distributional reinforcement learning

Cooperative optimization of energy and thermal management in PHEV using a time-correlated Laplacian noise-based reinforcement learning framework

Multi-task deep reinforcement learning for service-oriented post-flood recovery scheduling of large urban metro systems

Educators’ Discussion with a Generative Artificial Intelligence Trainer

It is learned that genAI was created to be people pleasing which can override its ability to find truth, and how it supports error analysis in Large Language Models, and how the public should view these tools.

PPO-STGNN: A Proximal Policy Optimization Approach with Spatio-Temporal Graph Neural Networks for DAG Task Scheduling in Cloud-Edge-End Computing

With the rapid development of the Internet of Things, computation intensive directed acyclic graph (DAG) tasks have become increasingly common in cloud-edge-end collaborative environments. However, cloud, edge, and end nodes are highly heterogeneous in computing capacity, network bandwidth, and energy consumption, whic...

Geometric Topology Optimization Algorithms

This paper introduces Geometric Topology Optimization Algorithms (GTOA) – a novel approach that integrates deep learning with topology optimization to accelerate complex structural simulations and design. We propose a novel "拓扑感知" algorithm utilizing Generative Adversarial Networks (GANs) to learn the geometric str...

Adaptive Learning-Based Robot Motion Planning

This paper presents a novel approach to robot motion planning based on adaptive learning. Traditional robot motion planning methods often struggle to cope with dynamic and uncertain environments. This research leverages reinforcement learning (RL) to develop a system capable of autonomously learning optimal motion stra...

Explainable Reinforcement Learning with Causal Inference

This paper explores a novel approach to reinforcement learning (RL) that integrates causal inference to enhance both the reliability and interpretability of decision-making processes. Traditional RL algorithms often operate as "black boxes," lacking transparency into the reasoning behind their actions. This research ad...

Dynamic Semantic Network Generator

This paper introduces a Dynamic Semantic Network Generator (DSNG), a novel system designed to automatically construct and maintain semantic networks representing complex, dynamic systems. The core of the DSNG is a reinforcement learning (RL) framework that adapts the network's structure and connections in response to c...

Dynamic Context-Aware Neural Architecture Search (DCA-NAS)

This paper introduces Dynamic Context-Aware Neural Architecture Search (DCA-NAS), a novel approach to neural architecture search that addresses the limitations of traditional NAS methods by incorporating dynamic contextual information during the search process. The core claim of DCA-NAS is that real-time analysis of in...

Neural Network Dynamics: Topology Self-Organization

This paper investigates the potential of dynamically self-organizing neural networks inspired by biological systems. Traditional static neural networks often struggle with adaptability and efficiency in complex environments. We propose a novel approach that leverages reinforcement learning to dynamically adjust connect...