Playing 2048 with genetically selected neural nets. Google Slides presentation here.
from trainer import Genetic2048Trainer as GT
t = GT(iterations_per_model=300, experiment_name='largenet_weightedbreeding',
debug=True, score_algorithm=lambda x: x.valid_moves,
breeding_algorithm='score_weighted', hidden_sizes=(16,8),
generations=1000, generation_size=1000, invalid_move_ends_game=True)
w_out = t.run_experiment()
To start a new experiment:
from trainer import TicTacToeTrainer as TT
t = TT(iterations_per_model=20, experiment_name='ttt_1',
debug=True, score_algorithm=lambda x: x.valid_moves * x.game_score,
breeding_algorithm='uniform',
hidden_sizes=(4,), inputs=9, outputs=9,
generations=1000, generation_size=1000, generation_cutoff=0.3,
invalid_move_ends_game=True)
w_out = t.run_experiment()
To resume an old experiment:
from trainer import TicTacToeTrainer as TT
# Use the same parameters for the trainer as when you started the experiment
# To be fixed later
t = TT(iterations_per_model=20, experiment_name='ttt_1',
debug=True, score_algorithm=lambda x: x.valid_moves * x.game_score,
breeding_algorithm='uniform',
hidden_sizes=(4,), inputs=9, outputs=9,
generations=1000, generation_size=1000, generation_cutoff=0.3,
invalid_move_ends_game=True)
# If you don’t want to overwrite epochs, set the epoch num on the trainer
t.epoch_num=45
w_out = t.resume_experiment()
# If you don’t care, don’t set it on the trainer and load the epoch directly
# For example if the old experiment name differs
w_out = t.resume_experiment(epoch_num=45, experiment_name='ttt_2')
To get stats for an experiment, you can specify an epoch or get all stats at once:
t.experiment_stats()
This will place a stats.csv file in the experiments folder of the experiment name.
Accepted parameters are:
- epoch_start - For the epoch number to start from
- experiment_name - If the name of the experiment is different than the experiment name of the trainer
- redo_epochs - If previous stats made should be overwritten