DeepSim is an optimization platform that can use advanced Reinforcement Learning (RL) methods to develop neural network-based controller software. DeepSim supports various RL libraries, including RLLib. In this talk, we discuss how RLLib, as well as the Tune hyperparameter optimizer, are used to develop controller software. Next, to the default set of features that RLLib offers, DeepSim offers its users a set of custom loggers, actions distributions and network architectures for improved performance of the controllers. The training runs, required to train the neural network, are executed on a Kubernetes based Ray cluster and can be monitored via command line interface tools as well as via TensorBoard. Finally, we show how the trained neural network can be exported, for example via Keras, to be deployed on target hardware.
All the above is demonstrated using two concrete examples, in the first the fuel efficiency of a Hybrid Electric Vehicle is optimized and in the second we develop cruise control software using the Ansys VRXPERIENCE autonomous driving simulator.