https://github.com/thisiscetin/ttt_qlearning
TicTacToe game with Double Q-learning.
https://github.com/thisiscetin/ttt_qlearning
qlearning reinforcement-learning reinforcement-learning-excercises tictactoe-game
Last synced: 8 months ago
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TicTacToe game with Double Q-learning.
- Host: GitHub
- URL: https://github.com/thisiscetin/ttt_qlearning
- Owner: thisiscetin
- License: mit
- Created: 2020-03-11T14:29:33.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2020-03-21T18:32:17.000Z (over 5 years ago)
- Last Synced: 2024-12-28T10:37:55.576Z (10 months ago)
- Topics: qlearning, reinforcement-learning, reinforcement-learning-excercises, tictactoe-game
- Language: C++
- Homepage:
- Size: 187 KB
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# TicTacToe game with Double Q-Learning
Aim of this project to build a model-free reinforcement learning algorithm (QLearning) that can play tic tac toe
better than a human does. When application runs, agent starts playing agains in two other agents. One opponent agent picks highly random moves while the other one makes a bit smarter moves.
At the same time you can play against the agent, by using numbers on the board between [0, 8]. 0 refers to the (1, 1) cell in the tictactoe board while 8 refers to (3, 3).
## Building
```
> cmake . && make
```
This command should create a binary in `bin/` folder name `game`.
## Running the game
After running the command from the base (ttt_qlearning) folder,
```
> bin/game
```
You will generate 3 agents playing TicTacToe.
- Agent A will be playing with Agent B.
- Agent A will be playing with Agent C.
And you will be promted to enter a number to mark on the board. While training you can play against Agent A yourself and see the improvement.
```
[agent a vs. b] agent 0 won %: 58.547, agent 1 won %: 41.0874 | agent 0 double table (action) size: 29936
[agent a vs. c] agent 0 won %: 51.6226, agent 1 won %: 48.2668 | agent 0 double table (action) size: 30034
-o-
xo-
x--
Enter pos [0-8]:
```
While training continues you can play the game continuously.
## References
- [Reinforcement Learning by Deepsense](https://deepsense.ai/what-is-reinforcement-learning-the-complete-guide/)
- [Double Q-Learning](https://towardsdatascience.com/double-q-learning-the-easy-way-a924c4085ec3)
- [Wikipedia Q-learning](https://en.wikipedia.org/wiki/Q-learning)
- [Wikipedia Tic Tac Toe](https://en.wikipedia.org/wiki/Tic-tac-toe)