Abstract: In sparse-reward, long-horizon domains, reinforcement learning (RL) often suffers from slow convergence and instability, complicating robotic manipulation. Previous heuristic-guided ...

With the wide application of UAVs in modern operations, efficient cooperative task assignment of heterogeneous UAVs under complex constraints has become crucial for enhancing mission success rates.

Customer Interaction Journey Optimization using Machine Learning and Meta-Heuristic Algorithms proposes a way to identify opportunities related to marketing each of a business's products, also ...

Parents visiting their children’s kindergarten class for the first time may think they’ve arrived at the wrong room, especially if they expect it to resemble the kindergarten they attended as ...

THIS IS A SIMPLE REINFORCEMENT LEARNING DEMO USING Q-LEARNING WITH A GRID WORLD ENVIRONMENT IN PYTHON AND TKINTER. THE AGENT LEARNS TO REACH A RANDOMLY GENERATED GOAL USING EPSILON-GREEDY ACTION ...

When I started my first degree fresh out of high school, I was flooded with advice about what to study and what career path to follow. The most significant piece of guidance was from one of my ...

I have to admit it: I’ve always been a nerd. I loved school. I loved university. And yes, I’m seriously contemplating a PhD, not for career advancement, but simply for the joy of diving deep into ...

Abstract: Federated learning (FL) is a promising distributed machine learning framework for mobile networks, where an aggregation server produces a global model by aggregating the local models from ...

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