How To Parallelize Search of Reinforcement Learning Hyperparameters Open Class
What you will learn in this Open Class
In this class we are going to see that learning parameters (also known as hyperparameters) play a key role when training a robot with Reinforcement Learning. Finding the proper parameters is usually a difficult task that requires many trials and error.
In this class, we are going to see how to parallelize a manual search of hyperparameters by using the Gym Computers of ROS Development Studio. Gym computers allow launching several training instances in parallel, each one training on its own simulation and set of hyperparameters. The results of each computer can be monitored in real time.
We are going to see a manual way of selecting parameters.
You will learn:
- RL training depends on training hyperparameters
- Finding the proper hyperparameters can require many trials
- How to use Gym Computers to parallelize hyperparameters search