AndreasKretschmer/Deep_Reinforcement_Learning_Thesis
Folders and files
| Name | Name | Last commit date | ||
|---|---|---|---|---|
Repository files navigation
## Reinforcement Learning: Breakout
Implementation of
- DQN - Agent
- A3C - Agent
to
## Prerequistes
You can install all the prerequistes for code use using
```text
pip install -r requirements.txt
```
## Instructions on Use
To run the code use
```python
python main.py
```
with arguments where arguments are given by
```text
usage: main.py [-h] -n NETWORK -m MODE
Train and test different networks on Space Invaders
optional arguments:
-h, --help show this help message and exit
-m MODEL, --model MODEL
Please specify the model you wish to use, either DQN or A3C
-n MODE, --mode MODE Please specify the mode you wish to run, either train or eval
```
For example, to test the pre-trained Deep Q Network architecture and view the network playing Breakout use
```text
python main.py -n DQN -m train
```