Gameplay by AI
Launch and play the game (human player)
- To play the game
python3 2048.py
- To see arguments
python3 2048.py -h
- To train your AI (the tuple network will be saved to the directory tupleNet/)
python3 2048.py --play=n --train=on -e=5000 -m=500
- To test your AI for one game round
python3 2048.py --play=n --train=off
- Press arrow keys or w/a/s/d to move the tiles up/left/down/right
- Press 'h' to get a hint from your AI (let it move the critical step for you)
- Press 'z' to see how your AI crack the game (lazy mode, auto play by AI)
- Auto play mode can be toggled by pressing 'z' again
Use combinations of tiles to extract the features of the game board.
By updating the state-value of features, the value states of the game board will be the sum of the value of the features. This mapping from combinations of tiles to the state-value is the value function.
Here, I implement 6644-tuple network.
I implement TD(0) after state learning.
The "after state" is like the Q(s, a) value.
- Add expectimax search to enhance the performance.
- Implement BitBoard to speed up training.
- Implementing DQN to extract features and train may be interesting as well.