Case Study IV: Tuned Reinforcement Learning (in Python)

Abstract

Similar to the example in Chap. 10, which considered tuning a Deep Neural Network (DNN), this chapter also deals with neural networks, but focuses on a different type of learning task: reinforcement learning. This increases the complexity, since any evaluation of the learning algorithm also involves the simulation of the respective environment. The learning algorithm is not just tuned with a static data set, but rather with dynamic feedback from the environment, in which an agent operates. The agent is controlled via the DNN. Also, the parameters of the reinforcement learning algorithm have to be considered in addition to the network parameters. Based on a simple example from the Keras documentation, we tune a DNN used for reinforcement learning of the inverse pendulum environment toy example. As a bonus, this chapter shows how the demonstrated tuning tools can be used to interface with and tune a learning algorithm that is implemented in Python.