Authors:
Roman Liessner
;
Jakob Schmitt
;
Ansgar Dietermann
and
Bernard Bäker
Affiliation:
Dresden Institute of Automobile Engineering, TU Dresden, George-Bähr-Straße 1c, 01069 Dresden and Germany
Keyword(s):
Deep Reinforcement Learning, Hyperparameter Optimization, Random Forest, Energy Management, Hybrid Electric Vehicle.
Related
Ontology
Subjects/Areas/Topics:
Agent Models and Architectures
;
Agents
;
Artificial Intelligence
;
Autonomous Systems
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computational Intelligence
;
Evolutionary Computing
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Industrial Applications of AI
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Machine Learning
;
Methodologies and Methods
;
Neural Networks
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Symbolic Systems
;
Theory and Methods
Abstract:
Reinforcement Learning is a framework for algorithms that learn by interacting with an unknown environment. In recent years, combining this approach with deep learning has led to major advances in various fields. Numerous hyperparameters – e.g. the learning rate – influence the learning process and are usually determined by testing some variations. This selection strongly influences the learning result and requires a lot of time and experience. The automation of this process has the potential to make Deep Reinforcement Learning available to a wider audience and to achieve superior results. This paper presents a model-based hyperparameter optimization of the Deep Deterministic Policy Gradients (DDPG) algorithm and demonstrates it with a hybrid vehicle energy management environment. In the given case, the hyperparameter optimization is able to double the gained reward value of the DDPG agent.