Modeling System Dynamics with Physics-Informed Neural Networks Based on Lagrangian Mechanics

no code implementations • 29 May 2020 • Manuel A. Roehrl, Thomas A. Runkler, Veronika Brandtstetter, Michel Tokic, Stefan Obermayer

In this paper, we present physics-informed neural ordinary differential equations (PINODE), a hybrid model that combines the two modeling techniques to overcome the aforementioned problems.

A Benchmark Environment Motivated by Industrial Control Problems

2 code implementations • 27 Sep 2017 • Daniel Hein, Stefan Depeweg, Michel Tokic, Steffen Udluft, Alexander Hentschel, Thomas A. Runkler, Volkmar Sterzing

On one hand, these benchmarks are designed to provide interpretable RL training scenarios and detailed insight into the learning process of the method on hand.

OpenAI Gym Reinforcement Learning (RL)

Batch Reinforcement Learning on the Industrial Benchmark: First Experiences

no code implementations • 20 May 2017 • Daniel Hein, Steffen Udluft, Michel Tokic, Alexander Hentschel, Thomas A. Runkler, Volkmar Sterzing

The Particle Swarm Optimization Policy (PSO-P) has been recently introduced and proven to produce remarkable results on interacting with academic reinforcement learning benchmarks in an off-policy, batch-based setting.

reinforcement-learning Reinforcement Learning (RL)

Introduction to the "Industrial Benchmark"

no code implementations • 12 Oct 2016 • Daniel Hein, Alexander Hentschel, Volkmar Sterzing, Michel Tokic, Steffen Udluft

A novel reinforcement learning benchmark, called Industrial Benchmark, is introduced.

reinforcement-learning Reinforcement Learning (RL)