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Learn.py
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import os
import pathlib
import socket
import hashlib
import pickle
from GameServer import GameServer
from Emuhawk import Emuhawk
from Joypad import get_missing_values
import utils
class Learn:
def __init__(self, model, weights_index, args, best_score=21.19, predict_finishline=None):
self.best_score = best_score
self.model = model
self.initial_weights = model.get_weights()
self.weights_index = weights_index
self.initial_shape = self.initial_weights[weights_index].shape
self.state = args.state
if predict_finishline:
self.predict_finishline = predict_finishline
else:
self.predict_finishline = args.predict_finishline
self.args = args
if self.state is None:
self.folder = 'weights'
else:
self.folder = 'weights_{}'.format(self.state)
if not os.path.isdir(os.path.join(os.getcwd(), self.folder)):
pathlib.Path(os.path.join(os.getcwd(), self.folder)).mkdir(parents=True, exist_ok=True)
def learn(self, weights, keep_hash=True):
"""
:param weights:
:param keep_hash:
:return:
"""
_weights = self.model.get_weights()
if keep_hash:
adjusted_weights = self.initial_weights[self.weights_index] - weights.reshape(self.initial_shape)
else:
adjusted_weights = weights
_weights[self.weights_index] = adjusted_weights.reshape(self.initial_shape)
if keep_hash:
hash_digest = hashlib.sha1(_weights[self.weights_index]).hexdigest()
filename = '{}/{}.txt'.format(self.folder, hash_digest)
if os.path.isfile(filename):
with open(filename, 'r') as f:
score = float(f.read().strip())
print(filename)
print('retrieving hashed score: {}'.format(score))
return score
self.model.set_weights(_weights)
g = GameServer(socket_autostart=True,
socket_ip=socket.gethostbyname(socket.gethostname()),
socket_port=9000 + os.getpid() % 1000)
e = Emuhawk(socket_ip=g.server.ip,
socket_port=g.server.port,
lua_script=self.args.lua,
config_file=self.args.bizhawk_config)
e.state = self.state
e.start()
g.server.create_connection(timeout=60)
missing = get_missing_values(self.model)
if self.predict_finishline:
predictor_finish_line = None
on_img_error = None
else:
predictor_finish_line = lambda _: False
on_img_error = utils.msg_to_time
x = g.ai(method='socket',
model=self.model,
missing=missing,
threshold=self.args.play_threshold,
bit_array=self.args.bit_array,
predictor_finish_line=predictor_finish_line,
on_img_error=on_img_error)
if keep_hash:
with open('{}/{}.txt'.format(self.folder, hash_digest), 'w') as f:
f.write(str(x))
with open('{}/{}.weights-{}'.format(self.folder, hash_digest, self.weights_index), 'wb') as f:
pickle.dump(_weights[self.weights_index], f)
print(x)
return x