backgammon/main.py

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import argparse
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import sys
import os
import time
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# Parse command line arguments
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parser = argparse.ArgumentParser(description="Backgammon games")
parser.add_argument('--episodes', action='store', dest='episode_count',
type=int, default=1000,
help='number of episodes to train')
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parser.add_argument('--model', action='store', dest='model',
default='default',
help='name of Tensorflow model to use')
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parser.add_argument('--eval-methods', action='store',
default=['random'], nargs='*',
help='specifies evaluation methods')
parser.add_argument('--eval', action='store_true',
help='evaluate the neural network with a random choice bot')
parser.add_argument('--bench-eval-scores', action='store_true',
help='benchmark scores of evaluation measures. episode counts and model specified as options are ignored.')
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parser.add_argument('--train', action='store_true',
help='train the neural network')
parser.add_argument('--eval-after-train', action='store_true', dest='eval_after_train',
help='evaluate after each training session')
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parser.add_argument('--play', action='store_true',
help='play with the neural network')
parser.add_argument('--start-episode', action='store', dest='start_episode',
type=int, default=0,
help='episode count to start at; purely for display purposes')
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parser.add_argument('--train-perpetually', action='store_true',
help='start new training session as soon as the previous is finished')
parser.add_argument('--list-models', action='store_true',
help='list all known models')
parser.add_argument('--board-rep', action='store', dest='board_rep',
help='name of board representation to use as input to neural network')
parser.add_argument('--verbose', action='store_true',
help='If set, a lot of stuff will be printed')
parser.add_argument('--ply', action='store', dest='ply', default='0',
help='defines the amount of ply used when deciding what move to make')
parser.add_argument('--repeat-eval', action='store', dest='repeat_eval', default='1',
help='the amount of times the evaluation method should be repeated')
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args = parser.parse_args()
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config = {
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'model': args.model,
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'episode_count': args.episode_count,
'eval_methods': args.eval_methods,
'train': args.train,
'play': args.play,
'eval': args.eval,
'bench_eval_scores': args.bench_eval_scores,
'eval_after_train': args.eval_after_train,
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'start_episode': args.start_episode,
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'train_perpetually': args.train_perpetually,
'model_storage_path': 'models',
'bench_storage_path': 'bench',
'board_representation': args.board_rep,
'global_step': 0,
'verbose': args.verbose,
'ply': args.ply,
'repeat_eval': args.repeat_eval
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}
# Create models folder
if not os.path.exists(config['model_storage_path']):
os.makedirs(config['model_storage_path'])
model_path = lambda: os.path.join(config['model_storage_path'], config['model'])
# Make sure directories exist
log_path = os.path.join(model_path(), 'logs')
if not os.path.isdir(model_path()):
os.mkdir(model_path())
if not os.path.isdir(log_path):
os.mkdir(log_path)
def save_config():
import yaml
# checkpoint_path = os.path.join(config['model_storage_path'], config['model'])
# config_path = os.path.join(checkpoint_path, 'config')
# with open(config_path, 'a+') as f:
# print("lol")
print(yaml.dump(config))
# Define helper functions
def log_train_outcome(outcome, diff_in_values, trained_eps = 0, log_path = os.path.join(model_path(), 'logs', "train.log")):
format_vars = { 'trained_eps': trained_eps,
'count': len(outcome),
'sum': sum(outcome),
'mean': sum(outcome) / len(outcome),
'time': int(time.time()),
'average_diff_in_vals': diff_in_values/len(outcome)
}
with open(log_path, 'a+') as f:
f.write("{time};{trained_eps};{count};{sum};{mean};{average_diff_in_vals}".format(**format_vars) + "\n")
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def log_eval_outcomes(outcomes, trained_eps = 0, log_path = os.path.join(model_path(), 'logs', "eval.log")):
"""
:param outcomes:
:param average_diff_in_value:
:param trained_eps:
:param log_path:
:return:
"""
for outcome in outcomes:
scores = outcome[1]
format_vars = { 'commit': os.system('git describe --first-parent --always'),
'trained_eps': trained_eps,
'method': outcome[0],
'count': len(scores),
'sum': sum(scores),
'mean': sum(scores) / len(scores),
'time': int(time.time())
}
with open(log_path, 'a+') as f:
f.write("{commit};{time};{method};{trained_eps};{count};{sum};{mean}".format(**format_vars) + "\n")
def log_bench_eval_outcomes(outcomes, log_path, index, time, trained_eps = 0):
for outcome in outcomes:
scores = outcome[1]
format_vars = { 'trained_eps': trained_eps,
'method': outcome[0],
'count': len(scores),
'sum': sum(scores),
'mean': sum(scores) / len(scores),
'time': time,
'index': index,
}
with open(log_path, 'a+') as f:
f.write("{method};{count};{index};{time};{sum};{mean}".format(**format_vars) + "\n")
def find_board_rep():
checkpoint_path = os.path.join(config['model_storage_path'], config['model'])
board_rep_path = os.path.join(checkpoint_path, "board_representation")
with open(board_rep_path, 'r') as f:
return f.read()
def board_rep_file_exists():
checkpoint_path = os.path.join(config['model_storage_path'], config['model'])
board_rep_path = os.path.join(checkpoint_path, "board_representation")
return os.path.isfile(board_rep_path)
def create_board_rep():
checkpoint_path = os.path.join(config['model_storage_path'], config['model'])
board_rep_path = os.path.join(checkpoint_path, "board_representation")
with open(board_rep_path, 'a+') as f:
f.write(config['board_representation'])
# Do actions specified by command-line
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if args.list_models:
def get_eps_trained(folder):
with open(os.path.join(folder, 'episodes_trained'), 'r') as f:
return int(f.read())
model_folders = [ f.path
for f
in os.scandir(config['model_storage_path'])
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if f.is_dir() ]
models = [ (folder, get_eps_trained(folder)) for folder in model_folders ]
sys.stderr.write("Found {} model(s)\n".format(len(models)))
for model in models:
sys.stderr.write(" {name}: {eps_trained}\n".format(name = model[0], eps_trained = model[1]))
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exit()
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if __name__ == "__main__":
# Set up network
from network import Network
save_config()
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# Set up variables
episode_count = config['episode_count']
if config['board_representation'] is None:
if board_rep_file_exists():
config['board_representation'] = find_board_rep()
else:
sys.stderr.write("Was not given a board_rep and was unable to find a board_rep file\n")
exit()
else:
if not board_rep_file_exists():
create_board_rep()
else:
if config['board_representation'] != find_board_rep():
sys.stderr.write("Board representation \"{given}\", does not match one in board_rep file, \"{board_rep}\"\n".
format(given = config['board_representation'], board_rep = find_board_rep()))
exit()
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if args.train:
network = Network(config, config['model'])
start_episode = network.episodes_trained
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while True:
train_outcome, diff_in_values = network.train_model(episodes = episode_count, trained_eps = start_episode)
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start_episode += episode_count
log_train_outcome(train_outcome, diff_in_values, trained_eps = start_episode)
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if config['eval_after_train']:
eval_outcomes = network.eval(trained_eps = start_episode)
log_eval_outcomes(eval_outcomes, trained_eps = start_episode)
if not config['train_perpetually']:
break
elif args.play:
network = Network(config, config['model'])
network.play_against_network()
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elif args.eval:
network = Network(config, config['model'])
for i in range(int(config['repeat_eval'])):
start_episode = network.episodes_trained
# Evaluation measures are described in `config`
outcomes = network.eval(config['episode_count'])
log_eval_outcomes(outcomes, trained_eps = start_episode)
# elif args.play:
# g.play(episodes = episode_count)
elif args.bench_eval_scores:
# Make sure benchmark directory exists
if not os.path.isdir(config['bench_storage_path']):
os.mkdir(config['bench_storage_path'])
config = config.copy()
config['model'] = 'bench'
network = Network(config, config['model'])
start_episode = network.episodes_trained
if start_episode == 0:
print("Model not trained! Beware of using non-existing models!")
exit()
sample_count = 20
episode_counts = [25, 50, 100, 250, 500, 1000, 2500, 5000,
10000, 20000]
def do_eval():
for eval_method in config['eval_methods']:
result_path = os.path.join(config['bench_storage_path'],
eval_method) + "-{}.log".format(int(time.time()))
for n in episode_counts:
for i in range(sample_count):
start_time = time.time()
# Evaluation measure to be benchmarked are described in `config`
outcomes = network.eval(episode_count = n)
time_diff = time.time() - start_time
log_bench_eval_outcomes(outcomes,
time = time_diff,
index = i,
trained_eps = start_episode,
log_path = result_path)
# CMM: oh no
import tensorflow as tf
network.restore_model()
do_eval()