Reinforcement Learning applied on the Bavarian card game 'Schafkopf'
Player 1 (RL bot) is learning, while players 2-4 are acting random. Afterwards update players 2-4 so they act according to the Q Network while player 1 is learning again.
| module | content |
|---|---|
| rules.py | definition of cards, scores, games, rewards and helper methods |
| state_overall.py | states which are valid for every player |
| state_player.py | states which are valid for single player |
| train_select_game.py | hyperparameters for NNs, training Q Networks |
| train_select_cards.py | hyperparameters for NNs, training Q Networks |
| QL_select_game.py | Q Network architechture and Memory for selecting game |
| QL_select_card.py | Q Network architechture and Memory for selecting cards |
| interface_to_states.py | managing states for each player and overall states |
| script.py | script |
| helper_functions.py | e.g. plot functions |
| Action | |
|---|---|
| set state davongelaufen | identify if player davongelaufen in play() |
| QL for selecting cards | |
| QL for doppeln | |
| Stock | |
| select game process simplified right now | |
| Tout, Sie | |
| Contra |
Player 1 = choosing game based to dealed cards as a state and NN, but playing cards random
Players 2-4 = acting completely random
