In order to learn about DP and RL, I chose to start a side project where I would train an AI to play a "simple" card game. I will be doing this using the DQN with replay memory.
The problem is, I can't get the intuition behind how to represent the input to the neural network..
About the game
It's a fairly simple 2-players game. There is a deck of 40 unique cards (4 types of cards, 10 numbered cards in each type).
Each player gets 4 cards and each turn a player must put a card on the table.
If a player puts a card and there is already a card with the same number on the table, the player wins both cards.
If for example a player plays Card 2 and on the table there is Cards 2, 3, 4, 5 then the player wins all those cards (sequence).
Cards won don't go back to the hand nor to the deck, they are just kept as like a score.
When the players have 0 cards in hand, another 4 cards are dealt to each one untile the deck has 0 cards left where then we decide who won based on the number of cards eaten/won.
As the input, I will be using the following:
- Current cards in the AI hand (40 one-hot-encoded features?)
- Current cards on the table (40 one-hot-encoded features?)
- History of played cards (40 one-hot-encoded features?)
This would give 120 columns/features in each state.
I am wondering wheter this is too much for the NN or wheter my input representation would be bad for the NN?
Should the features be represented as a (120,) vector or as a 3x40 matrix?
I am also wondering if it's a good idea to represent the current cards on the table as just a 10 one-hot-encoded features since the type of the cards don't matter and the same number can't exist 2 times in the table?
Thank you in advance.