Reinforcement Learning Algorithms with Python

Creating your own environment

For educational purposes, in this book, we have predominantly used fast and small-scale tasks that could best fit our needs. However, there are plenty of simulators in existence for locomotion tasks (such as Gazebo, Roboschool, and Mujoco), mechanical engineering, transportation, self-driving cars, security, and many more. These existing environments are diverse, but there isn't one for every possible application. Thus, in some situations, you may find yourself in charge of creating your own.

The reward function by itself is difficult to design, but it is a key part of RL. With the wrong reward function, the environment can be impossible to solve and the agent may learn the wrong behaviors. In Chapter 1, The ...