In this a tutorial we explain how to use the fragile framework as an explorer runner useful to memorize high quality memory reply status in order to train a neural network in the OpenAI gym library. It covers how to instantiate all the training process using any Python Jupyter simply running all the cells.
This code has been designed and tested using the Google Colab environment: https://colab.research.google.com/
You should visit and understand before continuing the getting started tutorial
The main point after using here the fragile framework is the possibility of training a neural network model in any OpenAI Gym game without the necesity of using a huge random memory reply pack and neither the use of a suplementary target network as usually done in the DQN (Deep Q Learning) reinforcement learning technics.
With the use of the fragile framework we can direclty generate useful and "small" memory reply packs to use directly in the fit process of the model in a supervised learning way.
Note:
It's very important to understand that we don't use the reward of every step process. We use a imitation learning method where the model try to imitate what the best fragile framework walker inside the swarm made during its history tree.
This algorithm is able to reach using only a few training runs (and a very small memory reply set) the average score reached by other RL methods like DQN using millions of training steps and a very big memory reply set.
The test was made using the game: SpaceInvaders
Human average: ~372
DDQN average: ~479 (128%)
Ours average: ~500
In the game Atlantis, our code reach the human average score in more or less 4 training runs: ~25000
There are even a lot of hyperparameters to play with in order to improve these results ;).