Pownie/backgammon
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added some comments and removed some old code

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This commit is contained in:
Alexander Munch-Hansen 2018-04-22 19:13:46 +02:00
parent 77d82f6883
commit 160f5bd737
1 changed files with 29 additions and 7 deletions
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36
network.py
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@ -1,5 +1,4 @@
import tensorflow as tf
from cup import Cup
import numpy as np
from board import Board
import os
@ -77,10 +76,6 @@ class Network:
self.value = tf.sigmoid(tf.matmul(value_after_input, W_2) + b_2, name='output_layer')
# tf.reduce_sum basically finds the sum of its input, so this gives the
# difference between the two values, in case they should be lists, which
# they might be if our input changes
# 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))
@ -95,7 +90,6 @@ class Network:
with tf.variable_scope('apply_gradients'):
for gradient, trainable_var in zip(gradients, trainable_vars):
# Hopefully this is Δw_t = α(V_t+1 - V_t)▿_wV_t.
backprop_calc = self.learning_rate * difference_in_values * gradient
grad_apply = trainable_var.assign_add(backprop_calc)
apply_gradients.append(grad_apply)
@ -148,6 +142,10 @@ class Network:
def gen_21_rolls(self):
"""
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):
@ -187,7 +185,9 @@ class Network:
spec_roll_scores = []
all_rolls_boards = Board.calculate_legal_states(a_board, player*-1, roll)
spec_roll_scores.append([self.eval_state(sess, self.board_trans_func(new_board, player*-1)) for new_board in all_rolls_boards])
spec_roll_scores.append(
[self.eval_state(sess, self.board_trans_func(new_board, player*-1)) for new_board in all_rolls_boards]
)
best_score = max(spec_roll_scores)
@ -201,8 +201,27 @@ class Network:
return [best_board, max(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):
@ -337,6 +356,9 @@ class Network:
(random.randrange(1, 7), random.randrange(1, 7)),
player)
# print("The evaluation of the previous state:\n", self.eval_state(sess, self.board_trans_func(prev_board, player)))
# print("The evaluation of the current_state:\n", cur_board_value)
# adjust weights
sess.run(self.training_op,
feed_dict={self.x: self.board_trans_func(prev_board, player),