13 citations · 29 across the 4 of their papers we have counts for
4 papers · 1 filter
Hybrid Reinforcement Learning and Model Predictive Control for Adaptive Control of Hydrogen-Diesel Dual-Fuel Combustion
Julian Bedei, Murray McBain, Alexander Winkler +3
Reinforcement Learning (RL) and Machine Learning Integrated Model Predictive Control (ML-MPC) are promising approaches for optimizing hydrogen-diesel dual-fuel engine control, as t…
Safe Reinforcement Learning for Real-World Engine Control
Julian Bedei, Lucas Koch, Kevin Badalian +3
This work introduces a toolchain for applying Reinforcement Learning (RL), specifically the Deep Deterministic Policy Gradient (DDPG) algorithm, in safety-critical real-world envir…
LExCI: A Framework for Reinforcement Learning with Embedded Systems
Kevin Badalian, Lucas Koch, Tobias Brinkmann +4
Advances in artificial intelligence (AI) have led to its application in many areas of everyday life. In the context of control engineering, reinforcement learning (RL) represents a…
Transfer of Reinforcement Learning-Based Controllers from Model- to Hardware-in-the-Loop
Mario Picerno, Lucas Koch, Kevin Badalian +4
The process of developing control functions for embedded systems is resource-, time-, and data-intensive, often resulting in sub-optimal cost and solutions approaches. Reinforcemen…