Pownie
48a5f6cbb6
eventually do further plies, however some rewriting of the current "do_ply" will be needed, as described in a comment.
457 lines
19 KiB
Python
457 lines
19 KiB
Python
import tensorflow as tf
|
|
import numpy as np
|
|
from board import Board
|
|
import os
|
|
import time
|
|
import sys
|
|
import random
|
|
from eval import Eval
|
|
import glob
|
|
from operator import itemgetter
|
|
|
|
class Network:
|
|
# board_features_quack has size 28
|
|
# board_features_quack_fat has size 30
|
|
# board_features_tesauro has size 198
|
|
|
|
board_reps = {
|
|
'quack-fat' : (30, Board.board_features_quack_fat),
|
|
'quack' : (28, Board.board_features_quack),
|
|
'tesauro' : (198, Board.board_features_tesauro),
|
|
'quack-norm': (30, Board.board_features_quack_norm)
|
|
}
|
|
|
|
def custom_tanh(self, x, name=None):
|
|
return tf.scalar_mul(tf.constant(2.00), tf.tanh(x, name))
|
|
|
|
def __init__(self, config, name):
|
|
self.config = config
|
|
self.checkpoint_path = os.path.join(config['model_storage_path'], config['model'])
|
|
|
|
self.name = name
|
|
|
|
# Set board representation from config
|
|
self.input_size, self.board_trans_func = Network.board_reps[
|
|
self.config['board_representation']
|
|
]
|
|
self.output_size = 1
|
|
self.hidden_size = 40
|
|
self.max_learning_rate = 0.1
|
|
self.min_learning_rate = 0.001
|
|
|
|
self.global_step = tf.Variable(0, trainable=False, name="global_step")
|
|
self.learning_rate = tf.maximum(self.min_learning_rate,
|
|
tf.train.exponential_decay(self.max_learning_rate,
|
|
self.global_step, 50000,
|
|
0.96,
|
|
staircase=True),
|
|
name="learning_rate")
|
|
|
|
|
|
|
|
# Restore trained episode count for model
|
|
episode_count_path = os.path.join(self.checkpoint_path, "episodes_trained")
|
|
if os.path.isfile(episode_count_path):
|
|
with open(episode_count_path, 'r') as f:
|
|
self.episodes_trained = int(f.read())
|
|
else:
|
|
self.episodes_trained = 0
|
|
|
|
self.x = tf.placeholder('float', [1, self.input_size], name='input')
|
|
self.value_next = tf.placeholder('float', [1, self.output_size], name="value_next")
|
|
|
|
xavier_init = tf.contrib.layers.xavier_initializer()
|
|
|
|
W_1 = tf.get_variable("w_1", (self.input_size, self.hidden_size),
|
|
initializer=xavier_init)
|
|
W_2 = tf.get_variable("w_2", (self.hidden_size, self.output_size),
|
|
initializer=xavier_init)
|
|
|
|
b_1 = tf.get_variable("b_1", (self.hidden_size,),
|
|
initializer=tf.zeros_initializer)
|
|
b_2 = tf.get_variable("b_2", (self.output_size,),
|
|
initializer=tf.zeros_initializer)
|
|
|
|
|
|
value_after_input = tf.sigmoid(tf.matmul(self.x, W_1) + b_1, name='hidden_layer')
|
|
|
|
self.value = tf.sigmoid(tf.matmul(value_after_input, W_2) + b_2, name='output_layer')
|
|
|
|
# TODO: Alexander thinks that self.value will be computed twice (instead of once)
|
|
difference_in_values = tf.reshape(tf.subtract(self.value_next, self.value, name='difference_in_values'), [])
|
|
tf.summary.scalar("difference_in_values", tf.abs(difference_in_values))
|
|
|
|
trainable_vars = tf.trainable_variables()
|
|
gradients = tf.gradients(self.value, trainable_vars)
|
|
|
|
apply_gradients = []
|
|
|
|
global_step_op = self.global_step.assign_add(1)
|
|
|
|
|
|
with tf.variable_scope('apply_gradients'):
|
|
for gradient, trainable_var in zip(gradients, trainable_vars):
|
|
backprop_calc = self.learning_rate * difference_in_values * gradient
|
|
grad_apply = trainable_var.assign_add(backprop_calc)
|
|
apply_gradients.append(grad_apply)
|
|
|
|
|
|
with tf.control_dependencies([global_step_op]):
|
|
|
|
self.training_op = tf.group(*apply_gradients, name='training_op')
|
|
|
|
self.saver = tf.train.Saver(max_to_keep=1)
|
|
|
|
def eval_state(self, sess, state):
|
|
return sess.run(self.value, feed_dict={self.x: state})
|
|
|
|
def save_model(self, sess, episode_count, global_step):
|
|
self.saver.save(sess, os.path.join(self.checkpoint_path, 'model.ckpt'), global_step=global_step)
|
|
with open(os.path.join(self.checkpoint_path, "episodes_trained"), 'w+') as f:
|
|
print("[NETWK] ({name}) Saving model to:".format(name=self.name),
|
|
os.path.join(self.checkpoint_path, 'model.ckpt'))
|
|
f.write(str(episode_count) + "\n")
|
|
|
|
def restore_model(self, sess):
|
|
"""
|
|
Restore a model for a session, such that a trained model and either be further trained or
|
|
used for evaluation
|
|
|
|
:param sess: Current session
|
|
:return: Nothing. It's a side-effect that a model gets restored for the network.
|
|
"""
|
|
|
|
if glob.glob(os.path.join(self.checkpoint_path, 'model.ckpt*.index')):
|
|
|
|
latest_checkpoint = tf.train.latest_checkpoint(self.checkpoint_path)
|
|
print("[NETWK] ({name}) Restoring model from:".format(name=self.name),
|
|
str(latest_checkpoint))
|
|
self.saver.restore(sess, latest_checkpoint)
|
|
variables_names = [v.name for v in tf.trainable_variables()]
|
|
values = sess.run(variables_names)
|
|
for k, v in zip(variables_names, values):
|
|
print("Variable: ", k)
|
|
print("Shape: ", v.shape)
|
|
print(v)
|
|
|
|
# Restore trained episode count for model
|
|
episode_count_path = os.path.join(self.checkpoint_path, "episodes_trained")
|
|
if os.path.isfile(episode_count_path):
|
|
with open(episode_count_path, 'r') as f:
|
|
self.config['start_episode'] = int(f.read())
|
|
elif glob.glob(os.path.join(os.path.join(self.config['model_storage_path'], "baseline_model"), 'model.ckpt*.index')):
|
|
checkpoint_path = os.path.join(self.config['model_storage_path'], "baseline_model")
|
|
latest_checkpoint = tf.train.latest_checkpoint(checkpoint_path)
|
|
print("[NETWK] ({name}) Restoring model from:".format(name=self.name),
|
|
str(latest_checkpoint))
|
|
self.saver.restore(sess, latest_checkpoint)
|
|
|
|
variables_names = [v.name for v in tf.trainable_variables()]
|
|
values = sess.run(variables_names)
|
|
for k, v in zip(variables_names, values):
|
|
print("Variable: ", k)
|
|
print("Shape: ", v.shape)
|
|
print(v)
|
|
else:
|
|
print("You need to have baseline_model inside models")
|
|
exit()
|
|
|
|
|
|
def make_move(self, sess, board, roll, player):
|
|
"""
|
|
Find the best move given a board, roll and a player, by finding all possible states one can go to
|
|
and then picking the best, by using the network to evaluate each state. The highest score is picked
|
|
for the 1-player and the max(1-score) is picked for the -1-player.
|
|
|
|
:param sess:
|
|
:param board: Current board
|
|
:param roll: Current roll
|
|
:param player: Current player
|
|
:return: A pair of the best state to go to, together with the score of that state
|
|
"""
|
|
legal_moves = Board.calculate_legal_states(board, player, roll)
|
|
moves_and_scores = [(move, self.eval_state(sess, self.board_trans_func(move, player))) for move in legal_moves]
|
|
scores = [x[1] if np.sign(player) > 0 else 1-x[1] for x in moves_and_scores]
|
|
best_score_index = np.array(scores).argmax()
|
|
best_move_pair = moves_and_scores[best_score_index]
|
|
return best_move_pair
|
|
|
|
|
|
def calculate_2_ply(self, sess, board, roll, player):
|
|
"""
|
|
Find the best move based on a 2-ply look-ahead. First the best move is found for a single ply and then an
|
|
exhaustive search is performed on the best 15 moves from the single ply.
|
|
|
|
:param sess:
|
|
:param board:
|
|
:param roll: The original roll
|
|
:param player: The current player
|
|
:return: Best possible move based on 2-ply look-ahead
|
|
|
|
"""
|
|
|
|
init_legal_states = Board.calculate_legal_states(board, player, roll)
|
|
zero_ply_moves_and_scores = [(move, self.eval_state(sess, self.board_trans_func(move, player))) for move in init_legal_states]
|
|
|
|
# pythons reverse is in place and I can't call [:15] on it, without applying it to an object like so. Fuck.
|
|
best_fifteen = sorted(zero_ply_moves_and_scores, key=itemgetter(1))
|
|
|
|
|
|
# They're sorted from smallest to largest, therefore we wan't to reverse if the current player is 1, since
|
|
# player 1 wishes to maximize. It's not needed for player -1, since that player seeks to minimize.
|
|
if player == 1:
|
|
best_fifteen.reverse()
|
|
best_fifteen_boards = [x[0] for x in best_fifteen[:15]]
|
|
|
|
all_rolls_scores = self.do_ply(sess, best_fifteen_boards, player)
|
|
|
|
|
|
best_score_index = np.array(all_rolls_scores).argmax()
|
|
best_board = best_fifteen_boards[best_score_index]
|
|
|
|
return [best_board, max(all_rolls_scores)]
|
|
|
|
def do_ply(self, sess, boards, player):
|
|
"""
|
|
Calculates a single extra ply, resulting in a larger search space for our best move.
|
|
This is somewhat hardcoded to only do a single ply, seeing that it calls max on all scores, rather than
|
|
allowing the function to search deeper, which could result in an even larger search space. If we wish
|
|
to have more than 2-ply, this should be fixed, so we could extend this method to allow for 3-ply.
|
|
|
|
:param sess:
|
|
:param boards: The boards to try all rolls on
|
|
:param player: The player of the previous ply
|
|
:return: An array of scores where each index describes one of the boards which was given as param
|
|
to this function.
|
|
"""
|
|
|
|
def gen_21_rolls():
|
|
"""
|
|
Calculate all possible rolls, [[1,1], [1,2] ..]
|
|
:return: All possible rolls
|
|
"""
|
|
a = []
|
|
for x in range(1, 7):
|
|
for y in range(1, 7):
|
|
if not [x, y] in a and not [y, x] in a:
|
|
a.append([x, y])
|
|
|
|
return a
|
|
|
|
all_rolls = gen_21_rolls()
|
|
|
|
all_rolls_scores = []
|
|
for a_board in boards:
|
|
a_board_scores = []
|
|
for roll in all_rolls:
|
|
all_rolls_boards = Board.calculate_legal_states(a_board, player*-1, roll)
|
|
|
|
spec_roll_scores = [self.eval_state(sess, self.board_trans_func(new_board, player*-1))
|
|
for new_board in all_rolls_boards]
|
|
|
|
# We need 1-score for the -1 player
|
|
spec_roll_scores = [x if player == 1 else (1-x) for x in spec_roll_scores]
|
|
|
|
best_score = max(spec_roll_scores)
|
|
|
|
a_board_scores.append(best_score)
|
|
|
|
all_rolls_scores.append(sum(a_board_scores)/len(a_board_scores))
|
|
|
|
return all_rolls_scores
|
|
|
|
def eval(self, episode_count, trained_eps = 0, tf_session = None):
|
|
"""
|
|
Used to evaluate a model. Can either use pubeval, a model playing at an intermediate level, or dumbeval
|
|
a model which has been given random weights, so it acts deterministically random.
|
|
|
|
:param episode_count: The amount of episodes to run
|
|
:param trained_eps: The amount of episodes the model we want to evaluate, has trained
|
|
:param tf_session:
|
|
:return: outcomes: The outcomes of the evaluation session
|
|
"""
|
|
|
|
def do_eval(sess, method, episodes = 1000, trained_eps = 0):
|
|
"""
|
|
Do the actual evaluation
|
|
|
|
:param sess:
|
|
:param method: Either pubeval or dumbeval
|
|
:param episodes: Amount of episodes to use in the evaluation
|
|
:param trained_eps:
|
|
:return: outcomes : Described above
|
|
"""
|
|
|
|
start_time = time.time()
|
|
|
|
def print_time_estimate(eps_completed):
|
|
cur_time = time.time()
|
|
time_diff = cur_time - start_time
|
|
eps_per_sec = eps_completed / time_diff
|
|
secs_per_ep = time_diff / eps_completed
|
|
eps_remaining = (episodes - eps_completed)
|
|
sys.stderr.write(
|
|
"[EVAL ] Averaging {per_sec} episodes per second\n".format(per_sec=round(eps_per_sec, 2)))
|
|
sys.stderr.write(
|
|
"[EVAL ] {eps_remaining} episodes remaining; approx. {time_remaining} seconds remaining\n".format(
|
|
eps_remaining=eps_remaining, time_remaining=int(eps_remaining * secs_per_ep)))
|
|
|
|
sys.stderr.write(
|
|
"[EVAL ] Evaluating {eps} episode(s) with method '{method}'\n".format(eps=episodes, method=method))
|
|
|
|
|
|
if method == 'pubeval':
|
|
outcomes = []
|
|
for i in range(1, episodes + 1):
|
|
sys.stderr.write("[EVAL ] Episode {}".format(i))
|
|
board = Board.initial_state
|
|
while Board.outcome(board) is None:
|
|
roll = (random.randrange(1, 7), random.randrange(1, 7))
|
|
|
|
board = (self.make_move(sess, board, roll, 1))[0]
|
|
|
|
roll = (random.randrange(1, 7), random.randrange(1, 7))
|
|
|
|
board = Eval.make_pubeval_move(board, -1, roll)[0][0:26]
|
|
|
|
sys.stderr.write("\t outcome {}".format(Board.outcome(board)[1]))
|
|
outcomes.append(Board.outcome(board)[1])
|
|
sys.stderr.write("\n")
|
|
|
|
if i % 10 == 0:
|
|
print_time_estimate(i)
|
|
|
|
return outcomes
|
|
|
|
elif method == 'dumbeval':
|
|
outcomes = []
|
|
for i in range(1, episodes + 1):
|
|
sys.stderr.write("[EVAL ] Episode {}".format(i))
|
|
board = Board.initial_state
|
|
while Board.outcome(board) is None:
|
|
roll = (random.randrange(1, 7), random.randrange(1, 7))
|
|
|
|
board = (self.make_move(sess, board, roll, 1))[0]
|
|
|
|
roll = (random.randrange(1, 7), random.randrange(1, 7))
|
|
|
|
board = Eval.make_dumbeval_move(board, -1, roll)[0][0:26]
|
|
|
|
sys.stderr.write("\t outcome {}".format(Board.outcome(board)[1]))
|
|
outcomes.append(Board.outcome(board)[1])
|
|
sys.stderr.write("\n")
|
|
|
|
if i % 10 == 0:
|
|
print_time_estimate(i)
|
|
|
|
return outcomes
|
|
|
|
else:
|
|
sys.stderr.write("[EVAL ] Evaluation method '{}' is not defined\n".format(method))
|
|
return [0]
|
|
|
|
if tf_session == None:
|
|
with tf.Session() as session:
|
|
session.run(tf.global_variables_initializer())
|
|
self.restore_model(session)
|
|
outcomes = [ (method, do_eval(session,
|
|
method,
|
|
episode_count,
|
|
trained_eps = trained_eps))
|
|
for method
|
|
in self.config['eval_methods'] ]
|
|
return outcomes
|
|
else:
|
|
outcomes = [ (method, do_eval(tf_session,
|
|
method,
|
|
episode_count,
|
|
trained_eps = trained_eps))
|
|
for method
|
|
in self.config['eval_methods'] ]
|
|
return outcomes
|
|
|
|
def train_model(self, episodes=1000, save_step_size=100, trained_eps=0):
|
|
with tf.Session() as sess:
|
|
writer = tf.summary.FileWriter("/tmp/log/tf", sess.graph)
|
|
|
|
sess.run(tf.global_variables_initializer())
|
|
self.restore_model(sess)
|
|
|
|
variables_names = [v.name for v in tf.trainable_variables()]
|
|
values = sess.run(variables_names)
|
|
for k, v in zip(variables_names, values):
|
|
print("Variable: ", k)
|
|
print("Shape: ", v.shape)
|
|
print(v)
|
|
|
|
start_time = time.time()
|
|
|
|
def print_time_estimate(eps_completed):
|
|
cur_time = time.time()
|
|
time_diff = cur_time - start_time
|
|
eps_per_sec = eps_completed / time_diff
|
|
secs_per_ep = time_diff / eps_completed
|
|
eps_remaining = (episodes - eps_completed)
|
|
sys.stderr.write(
|
|
"[TRAIN] Averaging {per_sec} episodes per second\n".format(per_sec=round(eps_per_sec, 2)))
|
|
sys.stderr.write(
|
|
"[TRAIN] {eps_remaining} episodes remaining; approx. {time_remaining} seconds remaining\n".format(
|
|
eps_remaining=eps_remaining, time_remaining=int(eps_remaining * secs_per_ep)))
|
|
|
|
sys.stderr.write("[TRAIN] Training {} episodes and save_step_size {}\n".format(episodes, save_step_size))
|
|
outcomes = []
|
|
for episode in range(1, episodes + 1):
|
|
|
|
sys.stderr.write("[TRAIN] Episode {}".format(episode + trained_eps))
|
|
# TODO decide which player should be here
|
|
|
|
player = 1
|
|
prev_board = Board.initial_state
|
|
i = 0
|
|
while Board.outcome(prev_board) is None:
|
|
i += 1
|
|
|
|
cur_board, cur_board_value = self.make_move(sess,
|
|
prev_board,
|
|
(random.randrange(1, 7), random.randrange(1, 7)),
|
|
player)
|
|
|
|
|
|
|
|
# adjust weights
|
|
sess.run(self.training_op,
|
|
feed_dict={self.x: self.board_trans_func(prev_board, player),
|
|
self.value_next: cur_board_value})
|
|
|
|
player *= -1
|
|
|
|
prev_board = cur_board
|
|
|
|
final_board = prev_board
|
|
sys.stderr.write("\t outcome {}\t turns {}".format(Board.outcome(final_board)[1], i))
|
|
outcomes.append(Board.outcome(final_board)[1])
|
|
final_score = np.array([Board.outcome(final_board)[1]])
|
|
scaled_final_score = ((final_score + 2) / 4)
|
|
|
|
with tf.name_scope("final"):
|
|
merged = tf.summary.merge_all()
|
|
global_step, summary, _ = sess.run([self.global_step, merged, self.training_op],
|
|
feed_dict={self.x: self.board_trans_func(prev_board, player),
|
|
self.value_next: scaled_final_score.reshape((1, 1))})
|
|
writer.add_summary(summary, episode + trained_eps)
|
|
|
|
sys.stderr.write("\n")
|
|
|
|
if episode % min(save_step_size, episodes) == 0:
|
|
sys.stderr.write("[TRAIN] Saving model...\n")
|
|
self.save_model(sess, episode + trained_eps, global_step)
|
|
|
|
if episode % 50 == 0:
|
|
print_time_estimate(episode)
|
|
|
|
sys.stderr.write("[TRAIN] Saving model for final episode...\n")
|
|
self.save_model(sess, episode+trained_eps, global_step)
|
|
|
|
writer.close()
|
|
|
|
return outcomes |