Pownie/backgammon
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train and eval now outputs proper number of training episodes to log

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This commit is contained in:
Christoffer Müller Madsen 2018-03-09 21:05:38 +01:00
parent bd459ba0ad
commit fc88c64452
2 changed files with 23 additions and 27 deletions
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10
game.py
View File

@ -61,11 +61,11 @@ class Game:
self.board = p2.make_move(self.board, p2.get_sym(), roll)
def train_model(self, episodes=1000, save_step_size = 100, init_ep = 0):
def train_model(self, episodes=1000, save_step_size = 100, trained_eps = 0):
sys.stderr.write("[TRAIN] Training {} episodes and save_step_size {}\n".format(episodes, save_step_size))
outcomes = []
for episode in range(episodes):
sys.stderr.write("[TRAIN] Episode {}".format(episode + init_ep))
sys.stderr.write("[TRAIN] Episode {}".format(episode + trained_eps))
self.board = Board.initial_state
prev_board, prev_board_value = self.roll_and_find_best_for_bot()
@ -109,8 +109,8 @@ class Game:
print(self.board)
print("--------------------------------")
def eval(self, init_ep = 0):
def do_eval(method, episodes = 1000, init_ep = 0):
def eval(self, trained_eps = 0):
def do_eval(method, episodes = 1000, trained_eps = 0):
sys.stderr.write("[EVAL ] Evaluating {eps} episode(s) with method '{method}'\n".format(eps=episodes, method=method))
if method == 'random':
outcomes = []
@ -132,7 +132,7 @@ class Game:
return [ (method, do_eval(method,
self.config['episode_count'],
init_ep = init_ep))
trained_eps = trained_eps))
for method
in self.config['eval_methods'] ]

40
main.py
View File

@ -1,23 +1,27 @@
import argparse
import sys
import time
def print_train_outcome(outcome, init_ep = 0):
format_vars = { 'init_ep': init_ep,
def print_train_outcome(outcome, trained_eps = 0):
format_vars = { 'trained_eps': trained_eps,
'count': len(train_outcome),
'sum': sum(train_outcome),
'mean': sum(train_outcome) / len(train_outcome)}
print("train;{init_ep};{count};{sum};{mean}".format(**format_vars))
'mean': sum(train_outcome) / len(train_outcome),
'time': int(time.time())
}
print("train;{time};{trained_eps};{count};{sum};{mean}".format(**format_vars))
def print_eval_outcomes(outcomes, init_ep = 0):
def print_eval_outcomes(outcomes, trained_eps = 0):
for outcome in outcomes:
scores = outcome[1]
format_vars = { 'init_ep': init_ep,
format_vars = { 'trained_eps': trained_eps,
'method': outcome[0],
'count': len(scores),
'sum': sum(scores),
'mean': sum(scores) / len(scores)
'mean': sum(scores) / len(scores),
'time': int(time.time())
}
print("eval;{method};{init_ep};{count};{sum};{mean}".format(**format_vars))
print("eval;{time};{method};{trained_eps};{count};{sum};{mean}".format(**format_vars))
parser = argparse.ArgumentParser(description="Backgammon games")
parser.add_argument('--episodes', action='store', dest='episode_count',
@ -61,24 +65,16 @@ episode_count = args.episode_count
if args.train:
eps = 0
while True:
train_outcome = g.train_model(episodes = episode_count, init_ep = eps)
print_train_outcome(train_outcome, init_ep = eps)
if args.eval:
eval_outcomes = g.eval(init_ep = eps)
print_eval_outcomes(eval_outcomes, init_ep = eps)
train_outcome = g.train_model(episodes = episode_count, trained_eps = eps)
eps += episode_count
print_train_outcome(train_outcome, trained_eps = eps)
if args.eval:
eval_outcomes = g.eval(trained_eps = eps)
print_eval_outcomes(eval_outcomes, trained_eps = eps)
sys.stdout.flush()
elif args.eval:
outcomes = g.eval()
print_eval_outcomes(outcomes, init_ep = 0)
print_eval_outcomes(outcomes, trained_eps = 0)
#elif args.play:
# g.play(episodes = episode_count)
#outcomes = g.play(2000)
#print(outcomes)
#print(sum(outcomes))
#count = g.play()
# highest = max(highest,count)
# except KeyboardInterrupt:
# break
#print("\nHighest amount of turns is:",highest)