DQN Deep Q Network for discrete Action Spaces

I trained a DQN (Deep Q Network) to collect yellow bananas and to avoid collecting blue bananas.

I made the DQN in python with pytorch. I used Unity Machine Learning Agents for the environment.

In Reinforcement Learning the Agent needs to interact with the environment to learn, in this case the Agent receives a Reward of +1 for collecting a Yellow Banana, and a reward of -1 for collecting a blue banana.

I solved the environment:

If you want to look at the code:

DQN Network