hongshaorou/Tianshou
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

implement the training process

Browse Source
This commit is contained in:
rtz19970824 2017-12-21 23:30:24 +08:00
parent eda7ed07a1
commit 9ad53de54f
4 changed files with 114 additions and 23 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

1
.gitignore vendored
View File

@ -8,3 +8,4 @@ checkpoints
checkpoints_origin
*.json
.DS_Store
data

2
AlphaGo/game.py
View File

@ -60,7 +60,7 @@ class Game:
def think(self, latest_boards, color):
mcts = MCTS(self.game_engine, self.evaluator, [latest_boards, color], self.size ** 2 + 1, inverse=True)
mcts.search(max_step=1)
mcts.search(max_step=20)
temp = 1
prob = mcts.root.N ** temp / np.sum(mcts.root.N ** temp)
choice = np.random.choice(self.size ** 2 + 1, 1, p=prob).tolist()[0]

106
AlphaGo/model.py
View File

@ -2,6 +2,7 @@ import os
import time
import sys
import cPickle
from collections import deque
import numpy as np
import tensorflow as tf
@ -71,6 +72,13 @@ def value_head(input, is_training):
return h
class Data(object):
def __init__(self):
self.boards = []
self.probs = []
self.winner = 0
class ResNet(object):
def __init__(self, board_size, action_num, history_length=1, residual_block_num=20, checkpoint_path=None):
"""
@ -85,11 +93,18 @@ class ResNet(object):
self.board_size = board_size
self.action_num = action_num
self.history_length = history_length
self.checkpoint_path = checkpoint_path
self.x = tf.placeholder(tf.float32, shape=[None, self.board_size, self.board_size, 2 * self.history_length + 1])
self.is_training = tf.placeholder(tf.bool, shape=[])
self.z = tf.placeholder(tf.float32, shape=[None, 1])
self.pi = tf.placeholder(tf.float32, shape=[None, self.action_num])
self._build_network(residual_block_num, checkpoint_path)
self._build_network(residual_block_num, self.checkpoint_path)
# training hyper-parameters:
self.window_length = 1000
self.save_freq = 1000
self.training_data = {'states': deque(maxlen=self.window_length), 'probs': deque(maxlen=self.window_length),
'winner': deque(maxlen=self.window_length)}
def _build_network(self, residual_block_num, checkpoint_path):
"""
@ -118,7 +133,7 @@ class ResNet(object):
with tf.control_dependencies(self.update_ops):
self.train_op = tf.train.AdamOptimizer(1e-4).minimize(self.total_loss)
self.var_list = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES)
self.saver = tf.train.Saver(max_to_keep=10, var_list=self.var_list)
self.saver = tf.train.Saver(var_list=self.var_list)
self.sess = multi_gpu.create_session()
self.sess.run(tf.global_variables_initializer())
if checkpoint_path is not None:
@ -166,21 +181,90 @@ class ResNet(object):
state[0, :, :, 2 * self.history_length] = np.zeros([self.board_size, self.board_size])
return state
#TODO: design the interface between the environment and training
# TODO: design the interface between the environment and training
def train(self, mode='memory', *args, **kwargs):
if mode == 'memory':
pass
if mode == 'file':
self.train_with_file(data_path=kwargs['data_path'], checkpoint_path=kwargs['checkpoint_path'])
self._train_with_file(data_path=kwargs['data_path'], batch_size=kwargs['batch_size'],
checkpoint_path=kwargs['checkpoint_path'])
def train_with_file(self, data_path, checkpoint_path):
def _train_with_file(self, data_path, batch_size, checkpoint_path):
# check if the path is valid
if not os.path.exists(data_path):
raise ValueError("{} doesn't exist".format(data_path))
self.checkpoint_path = checkpoint_path
if not os.path.exists(self.checkpoint_path):
os.mkdir(self.checkpoint_path)
file_list = os.listdir(data_path)
if file_list <= 50:
time.sleep(1)
else:
file_list.sort(key=lambda file: os.path.getmtime(data_path + file) if not os.path.isdir(
data_path + file) else 0)
new_file_list = []
all_file_list = []
training_data = {}
iters = 0
while True:
new_file_list = list(set(os.listdir(data_path)).difference(all_file_list))
all_file_list = os.listdir(data_path)
new_file_list.sort(
key=lambda file: os.path.getmtime(data_path + file) if not os.path.isdir(data_path + file) else 0)
if new_file_list:
for file in new_file_list:
states, probs, winner = self._file_to_training_data(data_path + file)
assert states.shape[0] == probs.shape[0]
assert states.shape[0] == winner.shape[0]
self.training_data['states'].append(states)
self.training_data['probs'].append(probs)
self.training_data['winner'].append(winner)
training_data['states'] = np.concatenate(self.training_data['states'], axis=0)
training_data['probs'] = np.concatenate(self.training_data['probs'], axis=0)
training_data['winner'] = np.concatenate(self.training_data['winner'], axis=0)
if len(self.training_data['states']) != self.window_length:
continue
else:
data_num = training_data['states'].shape[0]
index = np.arange(data_num)
np.random.shuffle(index)
start_time = time.time()
value_loss, policy_loss, reg, _ = self.sess.run(
[self.value_loss, self.policy_loss, self.reg, self.train_op],
feed_dict={self.x: training_data['states'][index[:batch_size]],
self.z: training_data['winner'][index[:batch_size]],
self.pi: training_data['probs'][index[:batch_size]],
self.is_training: True})
print("Iteration: {}, Time: {}, Value Loss: {}, Policy Loss: {}, Reg: {}".format(iters,
time.time() - start_time,
value_loss,
policy_loss, reg))
iters += 1
if iters % self.save_freq == 0:
save_path = "Iteration{}.ckpt".format(iters)
self.saver.save(self.sess, self.checkpoint_path + save_path)
def _file_to_training_data(self, file_name):
with open(file_name, 'r') as file:
data = cPickle.load(file)
history = deque(maxlen=self.history_length)
states = []
probs = []
winner = []
for _ in range(self.history_length):
# Note that 0 is specified, need a more general way like config
history.append([0] * self.board_size ** 2)
# Still, +1 is specified
color = +1
for [board, prob] in zip(data.boards, data.probs):
history.append(board)
states.append(self._history2state(history, color))
probs.append(np.array(prob).reshape(1, self.board_size ** 2 + 1))
winner.append(np.array(data.winner).reshape(1, 1))
color *= -1
states = np.concatenate(states, axis=0)
probs = np.concatenate(probs, axis=0)
winner = np.concatenate(winner, axis=0)
return states, probs, winner
if __name__=="__main__":
model = ResNet(board_size=9, action_num=82)
model.train("file", data_path="./data/", batch_size=128, checkpoint_path="./checkpoint/")

28
AlphaGo/play.py
View File

@ -76,6 +76,7 @@ if __name__ == '__main__':
color = ['b', 'w']
pattern = "[A-Z]{1}[0-9]{1}"
space = re.compile("\s+")
size = 9
show = ['.', 'X', 'O']
@ -83,12 +84,20 @@ if __name__ == '__main__':
game_num = 0
try:
while True:
start_time = time.time()
num = 0
pass_flag = [False, False]
print("Start game {}".format(game_num))
# end the game if both palyer chose to pass, or play too much turns
while not (pass_flag[0] and pass_flag[1]) and num < size ** 2 * 2:
turn = num % 2
board = player[turn].run_cmd(str(num) + ' show_board')
board = eval(board[board.index('['):board.index(']') + 1])
for i in range(size):
for j in range(size):
print show[board[i * size + j]] + " ",
print "\n",
data.boards.append(board)
move = player[turn].run_cmd(str(num) + ' genmove ' + color[turn] + '\n')
print role[turn] + " : " + str(move),
num += 1
@ -102,21 +111,18 @@ if __name__ == '__main__':
play_or_pass = ' PASS'
pass_flag[turn] = True
result = player[1 - turn].run_cmd(str(num) + ' play ' + color[turn] + ' ' + play_or_pass + '\n')
board = player[turn].run_cmd(str(num) + ' show_board')
board = eval(board[board.index('['):board.index(']') + 1])
for i in range(size):
for j in range(size):
print show[board[i * size + j]] + " ",
print "\n",
data.boards.append(board)
prob = player[turn].run_cmd(str(num) + ' get_prob')
prob = space.sub(',', prob[prob.index('['):prob.index(']') + 1])
prob = prob.replace('[,', '[')
prob = prob.replace('],', ']')
prob = eval(prob)
data.probs.append(prob)
score = player[turn].run_cmd(str(num) + ' get_score')
print "Finished : ", score.split(" ")[1]
# TODO: generalize the player
if score > 0:
if eval(score.split(" ")[1]) > 0:
data.winner = 1
if score < 0:
if eval(score.split(" ")[1]) < 0:
data.winner = -1
player[0].run_cmd(str(num) + ' clear_board')
player[1].run_cmd(str(num) + ' clear_board')
@ -127,12 +133,12 @@ if __name__ == '__main__':
file_list.sort(key=lambda file: os.path.getmtime(args.result_path + file) if not os.path.isdir(
args.result_path + file) else 0)
data_num = eval(file_list[-1][:-4]) + 1
print(file_list)
with open("./data/" + str(data_num) + ".pkl", "w") as file:
picklestring = cPickle.dump(data, file)
data.reset()
game_num += 1
except KeyboardInterrupt:
print("Time {}".format(time.time()-start_time))
except Exception:
subprocess.call(["kill", "-9", str(agent_v0.pid)])
subprocess.call(["kill", "-9", str(agent_v1.pid)])
print "Kill all player, finish all game."