Scientific Research Publishing

Reinforcement Learning for Antidepressant Dose Adjustment: An Explainable Agent Approach ()

Reinforcement Learning, Explainable AI, Computational Psychiatry, Antidepressant Dose Optimization, Major Depressive Disorder, Treatment Personalization, Clinical Decision Support Share and Cite: de ...
IEEE

An Efficient Bayesian Policy Exploration Approach for Reinforcement Learning Model Predictive Control

Abstract: Reinforcement Learning Model Predictive Control (RL-MPC) has achieved significant progress in recent years. However, existing approaches still have some limitations. This paper proposes a ...
IEEE

Improving Floating Offshore Wind Farm Flow Control With Scalable Model-Based Deep Reinforcement Learning

Abstract: This paper proposes a model-based deep reinforcement learning (DRL) framework to maximize the total power output and minimize the fatigue load of a floating offshore wind farm subject to ...
Automation World

AI-Driven Motion Control: How Machine Learning Optimizes Motors, Reduces Downtime and Cuts Energy Costs

Automation experts from Beckhoff, DigiKey and Siemens Digital Industries explain how AI enhances motion control across applications in welding, automotive manufacturing and ...
gc.cuny

CS Seminar: Arthur Paul Pedersen and Saptarashmi Bandyopadhyay

Artificial Intelligence (AI) Agents are increasingly being deployed in Robotics, Augmented Reality/Virtual Reality, Self-Driving Cars, Scientific Discovery, Energy Orchestration, Computing Systems, ...
techxplore

Robots combine AI learning and control theory to perform advanced movements

When it comes to training robots to perform agile, single-task motor skills, such as handstands or backflips, artificial intelligence methods can be very useful. But if you want to train your robot to ...