I implemented Actor-Critic with N-step TD prediction to learn to play 2048 (link to the game : http://2048game.com/)
For the enviroment I don't use this 2048 implementation. I use a simple one without any graphical interface, just pure matrices. The input for the neural network is the log2 of the game board.
The structure of my network is:
1. Input layer
2. Hidden layer with 16 units
3. Softmax layer with 4 units (up, down, left, right) for the actor
4. Linear regression for the critic
The hidden layer is shared between the third and fourth layer.
The reward in the orginal game is the value of the merged cells. For example, if two fours merged than the reward is eight. My reward function is almost the same, except I take the log2 of it. I tried these parameteres and I also tweaked the learning rate, the gamma, but I couldn't achive any good result.
Could You recommend what should I change?