PyGame-based quadcopter simulator & Reinforcement Learning Project
APACHE-2.0 License
This repository provides code exercise and solution for Reinforcement Learning. All code is written in Python and uses custom RL environment from OpenAI Gym. Goal was train the Reinforcement Learning Drone Agent mastering the flying and accurately hitting a target balloon. A2C was used primaly, PPO and DQN also can be used. Explanations and details are down below ↓
Action space is discrete(5). It means there is certain 5 moves the Agent has to do. Do Nothing, Up, Down, Right, Left
$ pip install -r requirements.txt
$ python test.py
Default step is 300k. You can alter it from Constants.py (Loading Libraries may take a while)
$ python train.py
! After the training, your model will be saved in 'models' file. Evaluate your trained model with adding --model parameter to terminal, Or use pretrained models Which in models folder. There is a already pretrained model which named "Crayz_bill" and it is default model.
$ python evaluate.py
If you would like to evaluate your custom model:
$ python evaluate.py --model models/yourmodel
$ python evaluate.py --model models/200k