I'm wondering whether there are any papers that explain the thinking behind how RLPark is designed. Short of looking through the examples, I'm interested to know what's required to add a new problem. For instance, it looks like you need to specify the following:
1) Problem/Environment (e.g. Maze, MountainCar)
2) Actions (e.g. TabularActions)
3) Learning model (e.g. QLearning)
4) Acting policy (e.g. EpsilonGreedy)
5) Control (e.g. QLearningControl)
6) Agent (e.g. LearnerAgentFA)
7) Value function (e.g. MazeValueFunction)
Is there any documentation that specifies all the things that are required to solve certain kinds of problems?
Also, what is the motivation behind the project? Is it intended to be used for educational purposes, or to solve real-world problems? How should a new user think of it compared to something like RL-Glue/RL-Library and RLToolkit?