Completed The Fast Deep RL Course? Then you know how to solve simple problems using OpenAI Gym and Ray-RLlib. You also have a good basis in Deep RL.
The next obvious step is to apply this knowledge to real-world problems. At this juncture, it is common to face the following hurdles.
- No premade Gym environment exists for the problem you want to attack. You must define and create your own custom environment.
- After creating a custom environment, you apply an appropriate Deep RL algorithm with default framework settings. But the agent doesn't seem to learn anything or displays poor learning.
In this course, you will gain the skills needed to overcome these hurdles and become effective in real-world applications.
You will learn the main ideas behind designing and implementing custom Gym environments for real-world problems.
You will also learn how to apply several performance enhancing tricks and Ray Tune experiments to easily identify the most promising tricks. Here is a list of tricks we will cover.
- Redefining the observations and actions to cast the environment as close as possible to a Markov Decision Process
- Scaling observations, actions, and rewards to standard ranges preferred by Deep Neural Nets
- Shaping reward functions to help the agent better distinguish between good and bad actions
- Preprocessing the inputs to reduce complexity while preserving critical information
- Tuning the network size and hyperparameters of the Ray-RLlib algorithms to improve performance
Remember the shop inventory management problem from the The Fast Deep RL Course? That's an example of a real world problem.