This repository aims to build multiple reinforcement learning agents using different techniques to play the well-known 2048 game! As more and more methods are covered, I will try to compare the different approaches in terms of performance and probability of success.
Here's a quick demo of the RL agent playing the game reaching the 2048 tile!
Yeah, sadly it's not very good yet. A part of that is because I haven't been able to train the model for very long (I only have a MX150). So if anyone has a great GPU and wants to share their model, you're welcome to do so! I will try to train the model further, but I think it might be a better idea to try new methods.
Below are some analysis results based on 1000 games played by the RL Agent.
| Dist. of maximum tiles reached | Dist. of max score reached |
|---|---|
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- To play the game on your own:
$ python gui.py- To make the RL Agent play the game:
$ python algoTest.py --cfg cfgs/SimpleRL.yaml --viz- To re-compute the analysis results above:
$ python algoTest.py --cfg cfgs/SimpleRL.yaml --n_runs 1000- To train your own model (will save a checkpoint every 25 epochs):
$ python simpleRL.py- Build initial game code to allow human players
- Extend game mechanics to allow reinforcement learning agents
- Build a simple reinforcement learning agent to play the game
- Parallel gameplay during reinforcement learning training
- Improve the performance of the RL Agent