587 lines
23 KiB
Python
587 lines
23 KiB
Python
import tensorflow as tf
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import numpy as np
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from board import Board
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import os
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import time
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import sys
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import random
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from eval import Eval
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import glob
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from operator import itemgetter
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import tensorflow.contrib.eager as tfe
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class Network:
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# board_features_quack has size 28
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# board_features_quack_fat has size 30
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# board_features_tesauro has size 198
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board_reps = {
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'quack-fat' : (30, Board.board_features_quack_fat),
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'quack' : (28, Board.board_features_quack),
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'tesauro' : (198, Board.board_features_tesauro),
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'quack-norm': (30, Board.board_features_quack_norm)
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}
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def custom_tanh(self, x, name=None):
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return tf.scalar_mul(tf.constant(2.00), tf.tanh(x, name))
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def __init__(self, config, name):
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tf.enable_eager_execution()
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xavier_init = tf.contrib.layers.xavier_initializer()
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self.config = config
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self.checkpoint_path = os.path.join(config['model_storage_path'], config['model'])
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self.name = name
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# Set board representation from config
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self.input_size, self.board_trans_func = Network.board_reps[
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self.config['board_representation']
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]
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self.output_size = 1
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self.hidden_size = 40
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self.max_learning_rate = 0.1
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self.min_learning_rate = 0.001
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#tf.train.get_or_create_global_step()
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# Restore trained episode count for model
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episode_count_path = os.path.join(self.checkpoint_path, "episodes_trained")
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if os.path.isfile(episode_count_path):
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with open(episode_count_path, 'r') as f:
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self.episodes_trained = int(f.read())
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else:
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self.episodes_trained = 0
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global_step_path = os.path.join(self.checkpoint_path, "global_step")
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if os.path.isfile(global_step_path):
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with open(global_step_path, 'r') as f:
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self.global_step = int(f.read())
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else:
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self.global_step = 0
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self.model = tf.keras.Sequential([
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tf.keras.layers.Dense(40, activation="sigmoid", kernel_initializer=xavier_init,
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input_shape=(1,self.input_size)),
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tf.keras.layers.Dense(1, activation="sigmoid", kernel_initializer=xavier_init)
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])
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def exp_decay(self, max_lr, epi_counter, decay_rate, decay_steps):
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res = max_lr * decay_rate**(epi_counter // decay_steps)
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return res
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def do_backprop(self, prev_state, value_next):
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self.learning_rate = tf.maximum(self.min_learning_rate,
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self.exp_decay(self.max_learning_rate, self.global_step, 0.96, 50000),
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name="learning_rate")
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with tf.GradientTape() as tape:
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value = self.model(prev_state.reshape(1,-1))
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grads = tape.gradient(value, self.model.variables)
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difference_in_values = tf.reshape(tf.subtract(value_next, value, name='difference_in_values'), [])
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tf.summary.scalar("difference_in_values", tf.abs(difference_in_values))
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# global_step_op = self.global_step.assign_add(1)
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with tf.variable_scope('apply_gradients'):
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for grad, train_var in zip(grads, self.model.variables):
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backprop_calc = self.learning_rate * difference_in_values * grad
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train_var.assign_add(backprop_calc)
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def print_variables(self):
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variables = self.model.variables
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for k in variables:
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print(k)
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def eval_state(self, state):
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return self.model(state.reshape(1,-1))
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def save_model(self, episode_count):
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"""
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:param episode_count:
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:return:
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"""
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tfe.Saver(self.model.variables).save(os.path.join(self.checkpoint_path, 'model.ckpt'))
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#self.saver.save(sess, os.path.join(self.checkpoint_path, 'model.ckpt'), global_step=global_step)
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with open(os.path.join(self.checkpoint_path, "episodes_trained"), 'w+') as f:
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print("[NETWK] ({name}) Saving model to:".format(name=self.name),
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os.path.join(self.checkpoint_path, 'model.ckpt'))
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f.write(str(episode_count) + "\n")
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with open(os.path.join(self.checkpoint_path, "global_step"), 'w+') as f:
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print("[NETWK] ({name}) Saving global step to:".format(name=self.name),
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os.path.join(self.checkpoint_path, 'model.ckpt'))
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f.write(str(self.global_step) + "\n")
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if self.config['verbose']:
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self.print_variables()
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def calc_vals(self, states):
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values = self.model.predict_on_batch(states)
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return values
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def restore_model(self):
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"""
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Restore a model for a session, such that a trained model and either be further trained or
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used for evaluation
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:param sess: Current session
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:return: Nothing. It's a side-effect that a model gets restored for the network.
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"""
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if glob.glob(os.path.join(self.checkpoint_path, 'model.ckpt*.index')):
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latest_checkpoint = tf.train.latest_checkpoint(self.checkpoint_path)
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print("[NETWK] ({name}) Restoring model from:".format(name=self.name),
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str(latest_checkpoint))
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tfe.Saver(self.model.variables).restore(latest_checkpoint)
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# variables_names = [v.name for v in self.model.variables]
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# Restore trained episode count for model
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episode_count_path = os.path.join(self.checkpoint_path, "episodes_trained")
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if os.path.isfile(episode_count_path):
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with open(episode_count_path, 'r') as f:
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self.config['start_episode'] = int(f.read())
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global_step_path = os.path.join(self.checkpoint_path, "global_step")
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if os.path.isfile(global_step_path):
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with open(global_step_path, 'r') as f:
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self.config['global_step'] = int(f.read())
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if self.config['verbose']:
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self.print_variables()
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def make_move(self, board, roll, player):
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"""
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Find the best move given a board, roll and a player, by finding all possible states one can go to
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and then picking the best, by using the network to evaluate each state. The highest score is picked
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for the 1-player and the max(1-score) is picked for the -1-player.
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:param sess:
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:param board: Current board
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:param roll: Current roll
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:param player: Current player
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:return: A pair of the best state to go to, together with the score of that state
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"""
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legal_moves = list(Board.calculate_legal_states(board, player, roll))
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legal_states = [list(tmp) for tmp in legal_moves]
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legal_states = np.array([Board.board_features_quack_fat(tmp, player)[0] for tmp in legal_states])
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scores = self.model.predict_on_batch(legal_states)
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transformed_scores = [x if np.sign(player) > 0 else 1 - x for x in scores]
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best_score_idx = np.argmax(np.array(transformed_scores))
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best_move = legal_moves[best_score_idx]
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best_score = scores[best_score_idx]
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return [best_move, best_score]
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def make_move_n_ply(self, sess, board, roll, player, n = 1):
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best_pair = self.calc_n_ply(n, sess, board, player, roll)
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return best_pair
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def calculate_1_ply(self, sess, board, roll, player):
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"""
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Find the best move based on a 1-ply look-ahead. First the best move is found for a single ply and then an
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exhaustive search is performed on the best 15 moves from the single ply.
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:param sess:
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:param board:
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:param roll: The original roll
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:param player: The current player
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:return: Best possible move based on 1-ply look-ahead
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"""
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# find all legal states from the given board and the given roll
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init_legal_states = Board.calculate_legal_states(board, player, roll)
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# find all values for the above boards
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zero_ply_moves_and_scores = [(move, self.eval_state(sess, self.board_trans_func(move, player))) for move in init_legal_states]
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# pythons reverse is in place and I can't call [:15] on it, without applying it to an object like so. Fuck.
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best_fifteen = sorted(zero_ply_moves_and_scores, key=itemgetter(1), reverse=player==1)
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best_fifteen_boards = [x[0] for x in best_fifteen[:10]]
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all_rolls_scores = self.do_ply(sess, best_fifteen_boards, player)
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best_score_index = np.array(all_rolls_scores).argmax()
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best_board = best_fifteen_boards[best_score_index]
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return [best_board, max(all_rolls_scores)]
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def calc_n_ply(self, n_init, sess, board, player, roll):
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# find all legal states from the given board and the given roll
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init_legal_states = Board.calculate_legal_states(board, player, roll)
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# find all values for the above boards
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zero_ply_moves_and_scores = [(move, self.eval_state(sess, self.board_trans_func(move, player))) for move in init_legal_states]
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# pythons reverse is in place and I can't call [:15] on it, without applying it to an object like so. Fuck.
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sorted_moves_and_scores = sorted(zero_ply_moves_and_scores, key=itemgetter(1), reverse=player==1)
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best_boards = [x[0] for x in sorted_moves_and_scores[:10]]
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best_move_score_pair = self.n_ply(n_init, sess, best_boards, player)
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return best_move_score_pair
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def n_ply(self, n_init, sess, boards_init, player_init):
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def ply(n, boards, player):
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def calculate_possible_states(board):
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possible_rolls = [ (1, 1), (1, 2), (1, 3), (1, 4), (1, 5),
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(1, 6), (2, 2), (2, 3), (2, 4), (2, 5),
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(2, 6), (3, 3), (3, 4), (3, 5), (3, 6),
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(4, 4), (4, 5), (4, 6), (5, 5), (5, 6),
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(6, 6) ]
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# for roll in possible_rolls:
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# print(len(Board.calculate_legal_states(board, player, roll)))
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return [ Board.calculate_legal_states(board, player, roll)
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for roll
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in possible_rolls ]
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def find_best_state_score(boards):
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score_pairs = [ (board, self.eval_state(sess, self.board_trans_func(board, player)))
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for board
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in boards ]
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scores = [ pair[1]
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for pair
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in score_pairs ]
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best_score_pair = score_pairs[np.array(scores).argmax()]
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return best_score_pair
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def average_score(boards):
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return sum(boards)/len(boards)
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def average_ply_score(board):
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states_for_rolls = calculate_possible_states(board)
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best_state_score_for_each_roll = [
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find_best_state_score(states)
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for states
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in states_for_rolls ]
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best_score_for_each_roll = [ x[1]
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for x
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in best_state_score_for_each_roll ]
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average_score_var = average_score(best_score_for_each_roll)
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return average_score_var
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if n == 1:
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average_score_pairs = [ (board, average_ply_score(board))
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for board
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in boards ]
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return average_score_pairs
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elif n > 1: # n != 1
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def average_for_score_pairs(score_pairs):
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scores = [ pair[1]
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for pair
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in score_pairs ]
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return sum(scores)/len(scores)
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def average_plain(scores):
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return sum(scores)/len(scores)
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print("+"*20)
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print(n)
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print(type(boards))
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print(boards)
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possible_states_for_boards = [
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(board, calculate_possible_states(board))
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for board
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in boards ]
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average_score_pairs = [
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(inner_boards[0], average_plain([ average_for_score_pairs(ply(n - 1, inner_board, player * -1 if n == 1 else player))
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for inner_board
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in inner_boards[1] ]))
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for inner_boards
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in possible_states_for_boards ]
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return average_score_pairs
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else:
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assert False
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if n_init < 1: print("Unexpected argument n = {}".format(n_init)); exit()
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boards_with_scores = ply(n_init, boards_init, -1 * player_init)
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#print("Boards with scores:",boards_with_scores)
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scores = [ ( pair[1] if player_init == 1 else (1 - pair[1]) )
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for pair
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in boards_with_scores ]
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#print("All the scores:",scores)
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best_score_pair = boards_with_scores[np.array(scores).argmax()]
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return best_score_pair
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def do_ply(self, sess, boards, player):
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"""
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Calculates a single extra ply, resulting in a larger search space for our best move.
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This is somewhat hardcoded to only do a single ply, seeing that it calls max on all scores, rather than
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allowing the function to search deeper, which could result in an even larger search space. If we wish
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to have more than 2-ply, this should be fixed, so we could extend this method to allow for 3-ply.
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:param sess:
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:param boards: The boards to try all rolls on
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:param player: The player of the previous ply
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:return: An array of scores where each index describes one of the boards which was given as param
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to this function.
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"""
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def gen_21_rolls():
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"""
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Calculate all possible rolls, [[1,1], [1,2] ..]
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:return: All possible rolls
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"""
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a = []
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for x in range(1, 7):
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for y in range(1, 7):
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if not [x, y] in a and not [y, x] in a:
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a.append([x, y])
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return a
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all_rolls = gen_21_rolls()
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all_rolls_scores = []
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count = 0
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# loop over boards
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for a_board in boards:
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a_board_scores = []
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# loop over all rolls, for each board
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for roll in all_rolls:
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# find all states we can get to, given the board and roll and the opposite player
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all_rolls_boards = Board.calculate_legal_states(a_board, player*-1, roll)
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count += len(all_rolls_boards)
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# find scores for each board found above
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spec_roll_scores = [self.eval_state(sess, self.board_trans_func(new_board, player*-1))
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for new_board in all_rolls_boards]
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# if the original player is the -1 player, then we need to find (1-value)
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spec_roll_scores = [x if player == 1 else (1-x) for x in spec_roll_scores]
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# find the best score
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best_score = max(spec_roll_scores)
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# append the best score to a_board_scores, where we keep track of the best score for each board
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a_board_scores.append(best_score)
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# save the expected average of board scores
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all_rolls_scores.append(sum(a_board_scores)/len(a_board_scores))
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# return all the average scores
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print(count)
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return all_rolls_scores
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def eval(self, episode_count, trained_eps = 0):
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"""
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Used to evaluate a model. Can either use pubeval, a model playing at an intermediate level, or dumbeval
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a model which has been given random weights, so it acts deterministically random.
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:param episode_count: The amount of episodes to run
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:param trained_eps: The amount of episodes the model we want to evaluate, has trained
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:param tf_session:
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:return: outcomes: The outcomes of the evaluation session
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"""
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def do_eval(method, episodes = 1000, trained_eps = 0):
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"""
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Do the actual evaluation
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:param sess:
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:param method: Either pubeval or dumbeval
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:param episodes: Amount of episodes to use in the evaluation
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:param trained_eps:
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:return: outcomes : Described above
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"""
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start_time = time.time()
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def print_time_estimate(eps_completed):
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cur_time = time.time()
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time_diff = cur_time - start_time
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eps_per_sec = eps_completed / time_diff
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secs_per_ep = time_diff / eps_completed
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eps_remaining = (episodes - eps_completed)
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sys.stderr.write(
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"[EVAL ] Averaging {per_sec} episodes per second\n".format(per_sec=round(eps_per_sec, 2)))
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sys.stderr.write(
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"[EVAL ] {eps_remaining} episodes remaining; approx. {time_remaining} seconds remaining\n".format(
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eps_remaining=eps_remaining, time_remaining=int(eps_remaining * secs_per_ep)))
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sys.stderr.write(
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"[EVAL ] Evaluating {eps} episode(s) with method '{method}'\n".format(eps=episodes, method=method))
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if method == 'pubeval':
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outcomes = []
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for i in range(1, episodes + 1):
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sys.stderr.write("[EVAL ] Episode {}".format(i))
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board = Board.initial_state
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while Board.outcome(board) is None:
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roll = (random.randrange(1, 7), random.randrange(1, 7))
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board = (self.make_move(board, roll, 1))[0]
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roll = (random.randrange(1, 7), random.randrange(1, 7))
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board = Eval.make_pubeval_move(board, -1, roll)[0][0:26]
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sys.stderr.write("\t outcome {}".format(Board.outcome(board)[1]))
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outcomes.append(Board.outcome(board)[1])
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sys.stderr.write("\n")
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if i % 10 == 0:
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print_time_estimate(i)
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return outcomes
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elif method == 'dumbeval':
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outcomes = []
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for i in range(1, episodes + 1):
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sys.stderr.write("[EVAL ] Episode {}".format(i))
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board = Board.initial_state
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while Board.outcome(board) is None:
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roll = (random.randrange(1, 7), random.randrange(1, 7))
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board = (self.make_move(board, roll, 1))[0]
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roll = (random.randrange(1, 7), random.randrange(1, 7))
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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]
|
|
|
|
|
|
outcomes = [ (method, do_eval(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:
|
|
difference_in_vals = 0
|
|
|
|
self.restore_model()
|
|
|
|
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
|
|
self.global_step += 1
|
|
|
|
|
|
cur_board, cur_board_value = self.make_move(prev_board,
|
|
(random.randrange(1, 7), random.randrange(1, 7)),
|
|
player)
|
|
|
|
difference_in_vals += abs((cur_board_value - self.eval_state(self.board_trans_func(prev_board, player))))
|
|
|
|
if self.config['verbose']:
|
|
print("Difference in values:", difference_in_vals)
|
|
print("Current board value :", cur_board_value)
|
|
print("Current board is :\n",cur_board)
|
|
|
|
|
|
# adjust weights
|
|
if Board.outcome(cur_board) is None:
|
|
self.do_backprop(self.board_trans_func(prev_board, player), 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)
|
|
|
|
|
|
self.do_backprop(self.board_trans_func(prev_board, player), scaled_final_score.reshape(1,1))
|
|
|
|
|
|
|
|
sys.stderr.write("\n")
|
|
|
|
if episode % min(save_step_size, episodes) == 0:
|
|
sys.stderr.write("[TRAIN] Saving model...\n")
|
|
self.save_model(episode + trained_eps)
|
|
|
|
if episode % 50 == 0:
|
|
print_time_estimate(episode)
|
|
|
|
sys.stderr.write("[TRAIN] Saving model for final episode...\n")
|
|
self.save_model(episode+trained_eps)
|
|
|
|
return outcomes, difference_in_vals[0][0]
|
|
|
|
|