https://github.com/cpnota/all-example-project
An example project demonstrating how the Autonomous Learning Library can be used to build new reinforcement learning agents.
https://github.com/cpnota/all-example-project
Last synced: 3 months ago
JSON representation
An example project demonstrating how the Autonomous Learning Library can be used to build new reinforcement learning agents.
- Host: GitHub
- URL: https://github.com/cpnota/all-example-project
- Owner: cpnota
- Created: 2020-01-10T22:03:59.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2021-07-02T15:27:47.000Z (over 4 years ago)
- Last Synced: 2024-11-27T03:34:55.528Z (11 months ago)
- Language: Python
- Size: 58.6 KB
- Stars: 8
- Watchers: 2
- Forks: 3
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
## About
This repository shows how [Autonomous Learning Library](https://github.com/cpnota/autonomous-learning-library) can be used to build new reinforcement learning agents.
In particular, it contains a *model based* agent that predicts future frames and uses them to guide decision making.
## Instructions
First, you'll need the latest version of [Pytorch](https://pytorch.org).
If you wish to view [Tensorboard](https://github.com/tensorflow/tensorboard) logs, you'll also need to grab a copy of that (it also comes with `tensorflow`).
Then, you'll need to install the `autonomous-learning-library` along with the Atari environments:
```
pip install autonomous-learning-library[atari]
```
Unfortunately, the current IP holders for the Atari library made it more difficult to acquire a license and use the ROMs than it used to be.
If you have a license to use the ROMs, you can try [AutoROM](https://github.com/PettingZoo-Team/AutoROM).
## Usage
You can run the agent as well as a baseline DQN agent using:
```
python main.py Pong
```
You can track progress using:
```
tensorboard --logdir runs
```
Once the script has finished (could take a long time, especially if you do not have a fast GPU!), you can see the final results using:
```
python plot.py
```
## Results
For us, the above instructions produced the following results:
As you can see, this agent isn't very good!
On the other hand, the purpose of this agent was not performance,
but to demonstrate the utility of the `autonomous-learning-library` in developing *new* agents not included in the original library.
Maybe you can come up with ways of improving this agent!