SpaceShooter-Gym

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SpaceShooter-Gym

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Customer gym to use with OpenAI algorithms. Showing how you can create more test environments for your custom alorithms.

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<a href="https://www.youtube.com/claritycoders" target="_blank"><img src="https://i.imgur.com/sG7xxyc.png" title="Clarity Coders YouTube" /></a>
# Custom SpaceShooter-Gym
> Custom gym to use with OpenAI algorithms. Showing how you can create more test environments for your custom algorithms.
> Once you get your environment setup in the OpenAI Gym format it is super easy to switch between different test algorithms.

## Setup
- To use stable baselines you will need to install python 3.6 or 3.7
- Then install TensorFlow 1.15.0 ( Install GPU if you have one available or CPU to run on CPU )
```shell
pip install tensorflow==1.15      # CPU
pip install tensorflow-gpu==1.15  # GPU
```
- install OpenAI Gym and stable-baselines
```shell
pip install gym
pip install stable-baselines[mpi]
```
- install the rest of dependencies listed in requirments.txt

## Inputs (Observations)
- Uses inputs to the nural network (Observations) of pixes in the game include frames from the past. The shape is 252, 84, 1
- This allows our network to see which way enemies are moving to avoid collisions and shoot to score points.
<img src="https://i.imgur.com/OJ5JMUe.jpg" title="source: imgur.com" />

## Rewards
- The enviroment provides a reward to allow the network to learn from it's actions. 
- The reward I chose if fairly simple at the start of the frame the reward is -0.001. This means if the AI just stays alive and never shoots enemies it will have a low reward score. 
- If an enemy is hit the reward is increased by 1. 
- If you touch an enemy the game is over and your reward is -2.

## Project Structure
> Break down of the files in this GitHub

### Trained Models
- All the pre-trained models I use in the tutorial video are stored in the Trained-Models directory.

### Game Source Code
- The game source code is all stored inside of the src directory

### tmp
- This will save back ups of your model as you are training it in case the program would crash.

### SpaceENV.py
- This is the actual enviroment file that is used to create our project.
- This must follow the OpenAI Gym format.
- <a href="https://stable-baselines.readthedocs.io/en/master/guide/custom_env.html">More Info (stable-baselines)</a>

### Train-DQN.py and Train-PPO.py
- Examples of how to train your enviroment with different algorithms.

### run_env_PPO.py and run_env_random.py
- Examples of how to run your trained models. 
- Also an example of using a random agent to get a baseline to see how your trained models perform.

## Support

> Reach out to me at one of the following places!

- YouTube <a href="https://www.youtube.com/claritycoders" target="_blank">Clarity Coders</a>
- Chat with me! <a href="https://discord.gg/cAWW5qq" target="_blank">Discord</a>

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