final checkpoint

app.py

@@ -2,11 +2,27 @@ from flask import Flask, request, jsonify
 from flask_json import FlaskJSON, as_json_p
 from flask_cors import CORS
 from board import Board
+import tensorflow as tf
 from eval import Eval
+import argparse
 import main
 import random
 from network import Network
 
+parser = argparse.ArgumentParser(description="Backgammon games")
+
+parser.add_argument('--model', action='store', dest='model',
+                    default='player_testings',
+                    help='name of Tensorflow model to use')
+
+parser.add_argument('--board-rep', action='store', dest='board_rep',
+                    default='tesauro',
+                    help='name of board representation to use as input to neural network')
+
+
+args = parser.parse_args()
+
+
 app = Flask(__name__)
 
 
@@ -17,13 +33,14 @@ json = FlaskJSON(app)
 CORS(app)
 
 config = main.config.copy()
-config['model'] = "player_testings"
-config['ply'] = "0"
-config['board_representation'] = 'tesauro'
+config['model'] = args.model
+config['board_representation'] = args.board_rep
+
 network = Network(config, config['model'])
 
-network.restore_model()
-
+sess = tf.Session()
+sess.run(tf.global_variables_initializer())
+network.restore_model(sess)
 
 def calc_move_sets(from_board, roll, player):
     board = from_board
@@ -40,8 +57,8 @@ def calc_move_sets(from_board, roll, player):
 def tmp_name(from_board, to_board, roll, player, total_moves, is_quad=False):
     sets = calc_move_sets(from_board, roll, player)
     return_board = from_board
-    print("To board:\n",to_board)
-    print("All sets:\n",sets)
+    # print("To board:\n",to_board)
+    # print("All sets:\n",sets)
     for idx, board_set in enumerate(sets):
         board_set[0] = list(board_set[0])
         # print(to_board)
@@ -85,6 +102,32 @@ def check_move(prev, curr):
     return any(truth_list)
 
 
+@app.route('/pubeval_move', methods=['POST'])
+def pubeval_move():
+
+    data = request.get_json(force=True)
+    board = [int(x) for x in data['board'].split(',')]
+    player = int(data['player'])
+    roll = [int(x) for x in data['roll'].split(',')]
+
+    board, value = Eval.make_pubeval_move(tuple(board), player, roll)
+    print("Doing pubeval move")
+    return ",".join([str(x) for x in list(board)])
+
+
+@app.route('/network_move', methods=['POST'])
+def network_move():
+
+    data = request.get_json(force=True)
+    board = [int(x) for x in data['board'].split(',')]
+    player = int(data['player'])
+    roll = [int(x) for x in data['roll'].split(',')]
+
+    board, value = network.make_move(sess, tuple(board), roll, player)
+
+    print("Doing network move")
+    return ",".join([str(x) for x in list(board)])
+
 
 @app.route('/bot_move', methods=['POST'])
 def bot_move():
@@ -98,7 +141,7 @@ def bot_move():
     if use_pubeval:
         board, value = Eval.make_pubeval_move(tuple(board), 1, roll)
     else:
-        board, _ = network.make_move(tuple(board), roll, 1)
+        board, _ = network.make_move(sess, tuple(board), roll, 1)
 
     # print("Board!:",board)
 

bin/0-ply-tests.rb

@@ -1,78 +0,0 @@
-def run_stuff(board_rep, model_name, ply)
-  epi_count = 0
-  system("python3 main.py --train --model #{model_name} --board-rep #{board_rep} --episodes 1 --ply #{ply}")
-  while epi_count < 200000 do
-    system("python3 main.py --eval --model #{model_name} --eval-methods dumbeval --episodes 250 --ply #{ply} --repeat-eval 3")
-    system("python3 main.py --eval --model #{model_name} --eval-methods pubeval --episodes 250 --ply #{ply} --repeat-eval 3")
-    system("python3 main.py --train --model #{model_name} --episodes 2000 --ply #{ply}")
-    epi_count += 2000
-  end
-end
-
-
-### ///////////////////////////////////////////////////////////////
-# QUACK TESTINGS
-### ///////////////////////////////////////////////////////////////
-
-board_rep = "quack"
-model_name = "quack_test_0_ply"
-ply = 0
-
-run_stuff(board_rep, model_name, ply)
-
-
-# board_rep = "quack"
-# model_name = "quack_test_1_ply"
-# ply = 1
-
-# run_stuff(board_rep, model_name, ply)
-
-### ///////////////////////////////////////////////////////////////
-# QUACK-FAT TESTING
-### ///////////////////////////////////////////////////////////////
-
-board_rep = "quack-fat"
-model_name = "quack-fat_test_0_ply"
-ply = 0
-
-run_stuff(board_rep, model_name, ply)
-
-# board_rep = "quack-fat"
-# model_name = "quack-fat_test_1_ply"
-# ply = 1
-
-# run_stuff(board_rep, model_name, ply)
-
-### ///////////////////////////////////////////////////////////////
-# QUACK-NORM TESTING
-### ///////////////////////////////////////////////////////////////
-
-
-board_rep = "quack-norm"
-model_name = "quack-norm_test_0_ply"
-ply = 0
-
-run_stuff(board_rep, model_name, ply)
-
-# board_rep = "quack-norm"
-# model_name = "quack-norm_test_1_ply"
-# ply = 1
-
-# run_stuff(board_rep, model_name, ply)
-
-### ///////////////////////////////////////////////////////////////
-# TESAURO TESTING
-### ///////////////////////////////////////////////////////////////
-
-
-board_rep = "tesauro"
-model_name = "tesauro_test_0_ply"
-ply = 0
-
-run_stuff(board_rep, model_name, ply)
-
-# board_rep = "tesauro"
-# model_name = "tesauro_test_1_ply"
-# ply = 1
-
-# run_stuff(board_rep, model_name, ply)

bin/run_all_tests.rb

@@ -0,0 +1,90 @@
+def run_stuff(board_rep, model_name)
+  epi_count = 0
+  system("python3 main.py --train --model #{model_name} --board-rep #{board_rep} --episodes 1 --force-creation")
+  while epi_count < 200000 do
+    for _ in (1..3) do
+      system("python3 main.py --eval --model #{model_name} --board-rep #{board_rep} --eval-methods dumbeval --episodes 250")
+    end
+
+    for _ in (1..3) do
+      system("python3 main.py --eval --model #{model_name} --board-rep #{board_rep} --eval-methods pubeval --episodes 250")
+    end
+
+    system("python3 main.py --train --model #{model_name} --board-rep #{board_rep} --episodes 2000")
+    
+    epi_count += 2000
+  end
+end
+
+
+### ///////////////////////////////////////////////////////////////
+# QUACK TESTINGS
+### ///////////////////////////////////////////////////////////////
+
+board_rep = "quack"
+model_name = "quack_test_0_ply"
+
+
+#run_stuff(board_rep, model_name)
+
+
+#board_rep = "quack"
+#model_name = "quack_test_1_ply"
+#
+
+#run_stuff(board_rep, model_name)
+
+### ///////////////////////////////////////////////////////////////
+# QUACK-FAT TESTING
+### ///////////////////////////////////////////////////////////////
+
+board_rep = "quack-fat"
+model_name = "quack-fat_test_0_ply"
+
+
+#run_stuff(board_rep, model_name)
+
+#board_rep = "quack-fat"
+#model_name = "quack-fat_test_1_ply"
+#
+
+#run_stuff(board_rep, model_name)
+
+### ///////////////////////////////////////////////////////////////
+# QUACK-NORM TESTING
+### ///////////////////////////////////////////////////////////////
+
+
+board_rep = "quack-norm"
+model_name = "quack-norm_test_0_ply"
+
+
+#run_stuff(board_rep, model_name)
+
+#board_rep = "quack-norm"
+#model_name = "quack-norm_test_1_ply"
+#
+
+#run_stuff(board_rep, model_name)
+
+### ///////////////////////////////////////////////////////////////
+# TESAURO TESTING
+### ///////////////////////////////////////////////////////////////
+
+
+board_rep = "tesauro"
+model_name = "tesauro_test3_0_ply"
+
+
+run_stuff(board_rep, model_name)
+
+#board_rep = "tesauro"
+#model_name = "tesauro_test_1_ply"
+#
+
+#run_stuff(board_rep, model_name)
+
+
+
+
+

bin/train-evaluate-save

@@ -1,30 +1,30 @@
 #!/usr/bin/env ruby
-MODELS_DIR = 'models'
-
 def save(model_name)
   require 'date'
 
-  model_path = File.join(MODELS_DIR, model_name)
+  models_dir = 'models'
+  model_path = File.join(models_dir, model_name)
+  if not File.exists? model_path then
+    return false
+  end
 
   episode_count = (File.read File.join(model_path, 'episodes_trained')).to_i
 
   puts "Found model #{model_name} with episodes #{episode_count} trained!"
 
   file_name = "model-#{model_name}-#{episode_count}-#{Time.now.strftime('%Y%m%d-%H%M%S')}.tar.gz"
-  save_path = File.join(MODELS_DIR, 'saves', file_name)
+  save_path = File.join(models_dir, 'saves', file_name)
   puts "Saving to #{save_path}"
   
-  system("tar", "-cvzf", save_path, "-C", MODELS_DIR, model_name)
+  system("tar", "-cvzf", save_path, "-C", models_dir, model_name)
+
+  return true
 end
 
 def train(model, episodes)
   system("python3", "main.py", "--train", "--model", model, "--episodes", episodes.to_s)
 end
 
-def force_train(model, episodes)
-  system("python3", "main.py", "--train", "--force-creation", "--model", model, "--episodes", episodes.to_s)
-end
-
 def evaluate(model, episodes, method)
   system("python3", "main.py", "--eval" , "--model", model, "--episodes", episodes.to_s, "--eval-methods", method)
 end
@@ -33,37 +33,15 @@ model = ARGV[0]
 
 if model.nil? then raise "no model specified" end
 
-if not File.exists? File.join(MODELS_DIR, model) then
-  force_train model, 10
-  save model
-  3.times do
-    evaluate model, 250, "pubeval"
-  end
-  3.times do
-    evaluate model, 250, "dumbeval"
-  end
-end
-
-# while true do
-#   save model
-#   train model, 1000
-#   save model
-#   train model, 1000
-#   3.times do
-#     evaluate model, 250, "pubeval"
-#   end
-#   3.times do
-#     evaluate model, 250, "dumbeval"
-#   end
-# end
-
 while true do
   save model
-  train model, 500
-  5.times do
+  train model, 1000
+  save model
+  train model, 1000
+  3.times do
     evaluate model, 250, "pubeval"
   end
-  5.times do
+  3.times do
     evaluate model, 250, "dumbeval"
   end
 end

board.py

@@ -1,4 +1,3 @@
-import quack
 import numpy as np
 import itertools
 
@@ -13,9 +12,15 @@ class Board:
     
     @staticmethod
     def idxs_with_checkers_of_player(board, player):
-        return quack.idxs_with_checkers_of_player(board, player)
+        idxs = []
+        for idx, checker_count in enumerate(board):
+            if checker_count * player >= 1:
+                idxs.append(idx)
+        return idxs
 
-    
+
+    # TODO: Write a test for this
+    # TODO: Make sure that the bars fit, 0 represents the -1 player and 25 represents the 1 player
     # index 26 is player 1 home, index 27 is player -1 home
     @staticmethod
     def board_features_to_pubeval(board, player):
@@ -35,19 +40,19 @@ class Board:
     def board_features_quack(board, player):
         board = list(board)
         board += ([1, 0] if np.sign(player) > 0 else [0, 1])
-        return np.array(board).reshape(1,28)
+        return np.array(board).reshape(1, -1)
 
     # quack-fat
     @staticmethod
     def board_features_quack_fat(board, player):
-        return np.array(quack.board_features_quack_fat(board,player)).reshape(1,30)
-        # board = list(board)
-        # positives = [x if x > 0 else 0 for x in board]
-        # negatives = [x if x < 0 else 0 for x in board]
-        # board.append( 15 - sum(positives))
-        # board.append(-15 - sum(negatives))
-        # board += ([1, 0] if np.sign(player) > 0 else [0, 1])
-        # return np.array(board).reshape(1,30)
+        board = list(board)
+        positives = [x if x > 0 else 0 for x in board]
+        negatives = [x if x < 0 else 0 for x in board]
+        board.append( 15 - sum(positives))
+        board.append(-15 - sum(negatives))
+        board += ([1, 0] if np.sign(player) > 0 else [0, 1])
+        return np.array(board).reshape(1,-1)
+
 
     # quack-fatter
     @staticmethod
@@ -63,7 +68,7 @@ class Board:
         board.append(15 - sum(positives))
         board.append(-15 - sum(negatives))
         board += ([1, 0] if np.sign(player) > 0 else [0, 1])
-        return np.array(board).reshape(1, 30)
+        return np.array(board).reshape(1, -1)
 
     # tesauro
     @staticmethod
@@ -92,47 +97,35 @@ class Board:
             board_rep += bar_trans(board, player)
             board_rep += (15 - Board.num_of_checkers_for_player(board, player),)
 
-        board_rep += ([1, 0] if cur_player == 1 else [0, 1])
-
-        return np.array(board_rep).reshape(1, 198)
+        board_rep += ([1,0] if cur_player == 1 else [1,0])
 
+        return np.array(board_rep).reshape(1,198)
 
     @staticmethod
     def board_features_tesauro_fat(board, cur_player):
         def ordinary_trans(val, player):
             abs_val = val*player
             if abs_val <= 0:
-                return (0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0)
+                return (0, 0, 0, 0, 0, 0, 0, 0, 0)
             elif abs_val == 1:
-                return (1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0)
+                return (1, 0, 0, 0, 0, 0, 0, 0, 0)
             elif abs_val == 2:
-                return (1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0)
+                return (1, 1, 0, 0, 0, 0, 0, 0, 0)
             elif abs_val == 3:
-                return (1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0)
+                return (1, 1, 1, 0, 0, 0, 0, 0, 0)
             elif abs_val == 4:
-                return (1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0)
+                return (1, 1, 1, 1, 0, 0, 0, 0, 0)
             elif abs_val == 5:
-                return (1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0)
+                return (1, 1, 1, 1, 1, 0, 0, 0, 0)
             elif abs_val == 6:
-                return (1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0)
+                return (1, 1, 1, 1, 1, 1, 0, 0, 0)
             elif abs_val == 7:
-                return (1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0)
+                return (1, 1, 1, 1, 1, 1, 1, 0, 0)
             elif abs_val == 8:
-                return (1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0)
-            elif abs_val == 9:
-                return (1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0)
-            elif abs_val == 10:
-                return (1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0)
-            elif abs_val == 11:
-                return (1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0)
-            elif abs_val == 12:
-                return (1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0)
-            elif abs_val == 13:
-                return (1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0)
-            elif abs_val == 14:
-                return (1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0)
-            elif abs_val == 15:
-                return (1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1)
+                return (1, 1, 1, 1, 1, 1, 1, 1, 0)
+            else:
+                return (1, 1, 1, 1, 1, 1, 1, 1, (abs_val - 9) / 2)
+
 
         def bar_trans(board, player):
             if   player == 1: return (abs(board[0]/2),)
@@ -145,7 +138,7 @@ class Board:
             board_rep += bar_trans(board, player)
             board_rep += (15 - Board.num_of_checkers_for_player(board, player),)
 
-        board_rep += ([1, 0] if cur_player == 1 else [0, 1])
+        board_rep += ([1, 0] if cur_player == 1 else [1,0])
 
         return np.array(board_rep).reshape(1, len(board_rep))
 
@@ -172,15 +165,105 @@ class Board:
             # Calculate how many pieces there must be in the home state and divide it by 15
             features.append((15 - sum) / 15)
         features += ([1,0] if np.sign(cur_player) > 0 else [0,1])
-        test = np.array(features)
+        test = np.array(features).reshape(1,-1)
         #print("TEST:",test)
-        return test.reshape(1,198)
+        return test
 
 
 
     @staticmethod
     def is_move_valid(board, player, face_value, move):
-        return quack.is_move_valid(board, player, face_value, move)
+        if face_value == 0:
+            return True
+        else:
+            def sign(a):
+                return (a > 0) - (a < 0)
+
+            from_idx   = move[0]
+            to_idx     = move[1]
+            to_state   = None
+            from_state = board[from_idx]
+            delta      = to_idx - from_idx
+            direction  = sign(delta)
+            bearing_off = None
+
+            # FIXME: Use get instead of array-like indexing
+            if to_idx >= 1 and to_idx <= 24:
+                to_state   = board[to_idx]
+                bearing_off = False
+            else:  # Bearing off
+                to_state   = 0
+                bearing_off = True
+
+            # print("_"*20)
+            # print("board:", board)
+            # print("to_idx:", to_idx, "board[to_idx]:", board[to_idx], "to_state:", to_state)
+            # print("+"*20)
+        
+            def is_forward_move():
+                return direction == player
+
+            def face_value_match_move_length():
+                return abs(delta) == face_value
+
+            def bear_in_if_checker_on_bar():
+                if player == 1:
+                    bar = 0
+                else:
+                    bar = 25
+
+                bar_state = board[bar]
+
+                if bar_state != 0:
+                    return from_idx == bar
+                else:
+                    return True
+
+            def checkers_at_from_idx():
+                return sign(from_state) == player
+
+            def no_block_at_to_idx():
+                if -sign(to_state) == player:
+                    return abs(to_state) == 1
+                else:
+                    return True
+
+            def can_bear_off():
+                checker_idxs = Board.idxs_with_checkers_of_player(board, player)
+
+                def moving_directly_off():
+                    if player == 1:
+                        return to_idx == 25;
+                    if player == -1:
+                        return to_idx == 0;
+                
+                def is_moving_backmost_checker():
+                    if player == 1:
+                        return all([(idx >= from_idx) for idx in checker_idxs])
+                    else:
+                        return all([(idx <= from_idx) for idx in checker_idxs])
+
+                def all_checkers_in_last_quadrant():
+                    if player == 1:
+                        return all([(idx >= 19) for idx in checker_idxs])
+                    else:
+                        return all([(idx <= 6) for idx in checker_idxs])
+
+                return all([ moving_directly_off() or is_moving_backmost_checker(),
+                             all_checkers_in_last_quadrant() ])
+                
+            # TODO: add switch here instead of wonky ternary in all        
+            # print("is_forward:",is_forward_move())
+            # print("face_value:",face_value_match_move_length())
+            # print("Checkes_at_from:",checkers_at_from_idx())
+            # print("no_block:",no_block_at_to_idx())
+        
+            return all([ is_forward_move(),
+                         face_value_match_move_length(),
+                         bear_in_if_checker_on_bar(),
+                         checkers_at_from_idx(),
+                         no_block_at_to_idx(),
+                         can_bear_off() if bearing_off else True ])
 
     @staticmethod
     def any_move_valid(board, player, roll):
@@ -220,37 +303,40 @@ class Board:
 
 
     @staticmethod
-    def apply_moves_to_board(board, player, move):
-        from_idx = move[0]
-        to_idx = move[1]
-        board = list(board)
-        board[from_idx] -= player
-
-        if (to_idx < 1 or to_idx > 24):
-            return
-
-        if (board[to_idx] * player == -1):
-
-            if (player == 1):
-                board[25] -= player
-            else:
-                board[0] -= player
-
-            board[to_idx] = 0
-
-        board[to_idx] += player
-
-        return tuple(board)
+    def apply_moves_to_board(board, player, moves):
+        for move in moves:
+            from_idx, to_idx = move.split("/")
+            board[int(from_idx)] -= int(player)
+            board[int(to_idx)] += int(player)
+        return board
 
     @staticmethod
     def calculate_legal_states(board, player, roll):
         # Find all points with checkers on them belonging to the player
         # Iterate through each index and check if it's a possible move given the roll
 
+        # TODO: make sure that it is not possible to do nothing on first part of
+        #       turn and then do something with the second die
+        
         def calc_moves(board, face_value):
-            if face_value == 0:
+            idxs_with_checkers = Board.idxs_with_checkers_of_player(board, player)
+            if len(idxs_with_checkers) == 0:
                 return [board]
-            return quack.calc_moves(board, player, face_value)
+            boards = [(Board.do_move(board,
+                               player,
+                               (idx, idx + (face_value * player)))
+                       if Board.is_move_valid(board,
+                                              player,
+                                              face_value,
+                                              (idx, idx + (face_value * player)))
+                       else None)
+                      for idx in idxs_with_checkers]
+            # print("pls:",boards)
+            board_list = list(filter(None, boards))  # Remove None-values
+            # if len(board_list) == 0:
+            #     return [board]
+            # print("board list:", board_list)
+            return board_list
 
         # Problem with cal_moves: Method can return empty list (should always contain at least same board).
         #               *Update*: Seems to be fixed.
@@ -264,17 +350,23 @@ class Board:
         if not Board.any_move_valid(board, player, roll):
             return { board }
         dice_permutations = list(itertools.permutations(roll)) if roll[0] != roll[1] else [[roll[0]]*4]
-        #print("Permuts:",dice_permutations)
         # print("Dice permuts:",dice_permutations)
         for roll in dice_permutations:
             # Calculate boards resulting from first move
+            #print("initial board: ", board)
+            #print("roll:", roll)
             boards = calc_moves(board, roll[0])
+            #print("boards after first die: ", boards)
 
             for die in roll[1:]:
                 # Calculate boards resulting from second move
                 nested_boards = [calc_moves(board, die) for board in boards]
+                #print("nested boards: ", nested_boards)
                 boards = [board for boards in nested_boards for board in boards]
-
+                # What the fuck
+                #for board in boards:
+                #    print(board)
+                #    print("type__:",type(board))
                 # Add resulting unique boards to set of legal boards resulting from roll
 
                 #print("printing boards from calculate_legal_states: ", boards)
@@ -303,9 +395,9 @@ class Board:
         return """
   13  14  15  16  17  18               19  20  21  22  23  24
 +--------------------------------------------------------------------------+
-| {13}| {14}| {15}| {16}| {17}| {18}| bar -1: {25} | {19}| {20}| {21}| {22}| {23}| {24}| end  1: TODO|
+| {13}| {14}| {15}| {16}| {17}| {18}| bar -1: {25} | {19}| {20}| {21}| {22}| {23}| {24}| end -1: TODO|
 |---|---|---|---|---|---|------------|---|---|---|---|---|---|             |
-| {12}| {11}| {10}| {9}| {8}| {7}| bar  1: {0} | {6}| {5}| {4}| {3}| {2}| {1}| end -1: TODO|
+| {12}| {11}| {10}| {9}| {8}| {7}| bar  1: {0} | {6}| {5}| {4}| {3}| {2}| {1}| end  1: TODO|
 +--------------------------------------------------------------------------+
   12  11  10   9   8   7                6   5   4   3   2   1 
  """.format(*temp)
@@ -313,8 +405,42 @@ class Board:
     @staticmethod
     def do_move(board, player, move):
         # Implies that move is valid; make sure to check move validity before calling do_move(...)
-        return quack.do_move(board, player, move)
+        
+        def move_to_bar(board, to_idx):
+            board = list(board)
+            if player == 1:
+                board[25] -= player
+            else:
+                board[0] -= player
+                
+            board[to_idx] = 0
+            return board
 
+        # TODO: Moving in from bar is handled by the representation
+        # TODONE: Handle bearing off
+
+        from_idx = move[0]
+        #print("from_idx: ", from_idx)
+        to_idx = move[1]
+        #print("to_idx: ", to_idx)
+        # pdb.set_trace()
+        board = list(board) # Make mutable copy of board
+
+        # 'Lift' checker
+        board[from_idx] -= player
+
+        # Handle bearing off
+        if to_idx < 1 or to_idx > 24:
+            return tuple(board)
+        
+        # Handle hitting checkers
+        if board[to_idx] * player == -1:
+            board = move_to_bar(board, to_idx)
+
+        # Put down checker
+        board[to_idx] += player
+
+        return tuple(board)
 
     @staticmethod
     def flip(board):

bot.py

@@ -1,8 +1,24 @@
+from cup import Cup
+from network import Network
 from board import Board
 
+import tensorflow as tf
+import numpy as np
+import random
+
 class Bot:
-    def __init__(self, sym):
+    def __init__(self, sym, config = None, name = "unnamed"):
+        self.config = config
+        self.cup = Cup()
         self.sym = sym
+        self.graph = tf.Graph()
+
+        self.network = Network(config, name)
+        self.network.restore_model()
+
+    def restore_model(self):
+        with self.graph.as_default():
+            self.network.restore_model()
 
     def get_session(self):
         return self.session
@@ -10,60 +26,16 @@ class Bot:
     def get_sym(self):
         return self.sym
 
+    def get_network(self):
+        return self.network
 
-    def calc_move_sets(self, from_board, roll, player):
-        board = from_board
-        sets = []
-        total = 0
-        print("board!:",board)
-        for r in roll:
-            # print("Value of r:",r)
-            sets.append([Board.calculate_legal_states(board, player, [r,0]), r])
-            total += r
-        sets.append([Board.calculate_legal_states(board, player, [total,0]), total])
-        return sets
-
-
-    def handle_move(self, from_board, to_board, roll, player):
-
-        # print("Cur board:",board)
-        sets = self.calc_move_sets(from_board, roll, player)
-        for idx, board_set in enumerate(sets):
-            board_set[0] = list(board_set[0])
-            # print("My board_set:",board_set)
-            if to_board in [list(c) for c in board_set[0]]:
-                self.total_moves -= board_set[1]
-                if idx < 2:
-                    # print("Roll object:",self.roll)
-                    self.roll[idx] = 0
-                else:
-                    self.roll = [0,0]
-                break
-        print("Total moves left:",self.total_moves)
-
-
-    def tmp_name(self, from_board, to_board, roll, player, total_moves):
-        sets = self.calc_move_sets(from_board, roll, player)
-        return_board = from_board
-        for idx, board_set in enumerate(sets):
-            board_set = list(board_set[0])
-            if to_board in [list(board) for board in board_set]:
-                total_moves -= board_set[1]
-                # if it's not the sum of the moves
-                if idx < 2:
-                    roll[idx] = 0
-                else:
-                    roll = [0,0]
-                return_board = to_board
-                break
-        return total_moves, roll, return_board
-
-    def make_human_move(self, board, player, roll):
-        total_moves = roll[0] + roll[1]
-        previous_board = board
-        while total_moves != 0:
-            move = input("Pick a move!\n")
-            to_board = Board.apply_moves_to_board(previous_board, player, move)
-            total_moves, roll, board = self.tmp_name(board, to_board, roll, player, total_moves)
-
+    # TODO: DEPRECATE
+    def make_move(self, board, sym, roll):
+        # print(Board.pretty(board))
+        legal_moves = Board.calculate_legal_states(board, sym, roll)
+        moves_and_scores = [ (move, self.network.eval_state(np.array(move).reshape(1,26))) for move in legal_moves ]
+        scores = [ x[1] for x in moves_and_scores ]
+        best_move_pair = moves_and_scores[np.array(scores).argmax()]
+        #print("Found the best state, being:", np.array(move_scores).argmax())
+        return best_move_pair
         

main.py

@@ -2,7 +2,6 @@ import argparse
 import sys
 import os
 import time
-import subprocess
 
 # Parse command line arguments
 parser = argparse.ArgumentParser(description="Backgammon games")
@@ -32,17 +31,17 @@ parser.add_argument('--train-perpetually', action='store_true',
                     help='start new training session as soon as the previous is finished')
 parser.add_argument('--list-models', action='store_true',
                     help='list all known models')
+parser.add_argument('--force-creation', action='store_true',
+                    help='force model creation if model does not exist')
 parser.add_argument('--board-rep', action='store', dest='board_rep',
+                    default='tesauro',
                     help='name of board representation to use as input to neural network')
-parser.add_argument('--verbose', action='store_true',
-                    help='If set, a lot of stuff will be printed')
-parser.add_argument('--ply', action='store', dest='ply', default='0',
-                    help='defines the amount of ply used when deciding what move to make')
-parser.add_argument('--repeat-eval', action='store', dest='repeat_eval', default='1',
-                    help='the amount of times the evaluation method should be repeated')
 
 args = parser.parse_args()
 
+if args.model == "baseline_model":
+    print("Model name 'baseline_model' not allowed")
+    exit()
 
 config = {
     'model': args.model,
@@ -58,13 +57,8 @@ config = {
     'model_storage_path': 'models',
     'bench_storage_path': 'bench',
     'board_representation': args.board_rep,
-    'global_step': 0,
-    'verbose': args.verbose,
-    'ply': args.ply,
-    'repeat_eval': args.repeat_eval
 }
 
-
 # Create models folder
 if not os.path.exists(config['model_storage_path']):
     os.makedirs(config['model_storage_path'])
@@ -78,20 +72,19 @@ if not os.path.isdir(model_path()):
 if not os.path.isdir(log_path):
     os.mkdir(log_path)
 
+
 # Define helper functions
 def log_train_outcome(outcome, diff_in_values, trained_eps = 0, log_path = os.path.join(model_path(), 'logs', "train.log")):
-    commit = subprocess.run(['git', 'describe', '--first-parent', '--always'], stdout=subprocess.PIPE).stdout.decode('utf-8').rstrip()
     format_vars = { 'trained_eps': trained_eps,
                     'count': len(outcome),
                     'sum': sum(outcome),
                     'mean': sum(outcome) / len(outcome),
                     'time': int(time.time()),
-                    'average_diff_in_vals': diff_in_values,
-                    'commit': commit
+                    'average_diff_in_vals': diff_in_values/len(outcome)
     }
 
     with open(log_path, 'a+') as f:
-        f.write("{time};{trained_eps};{count};{sum};{mean};{average_diff_in_vals};{commit}".format(**format_vars) + "\n")
+        f.write("{time};{trained_eps};{count};{sum};{mean};{average_diff_in_vals}".format(**format_vars) + "\n")
     
 
 def log_eval_outcomes(outcomes, trained_eps = 0, log_path = os.path.join(model_path(), 'logs', "eval.log")):
@@ -102,12 +95,9 @@ def log_eval_outcomes(outcomes, trained_eps = 0, log_path = os.path.join(model_p
     :param log_path:
     :return:
     """
-    commit = subprocess.run(['git', 'describe', '--first-parent', '--always'], stdout=subprocess.PIPE).stdout.decode('utf-8').rstrip()
-    
     for outcome in outcomes:
         scores = outcome[1]
-        format_vars = { 'commit': commit,
-                        'trained_eps': trained_eps,
+        format_vars = { 'trained_eps': trained_eps,
                         'method': outcome[0],
                         'count': len(scores),
                         'sum': sum(scores),
@@ -115,10 +105,9 @@ def log_eval_outcomes(outcomes, trained_eps = 0, log_path = os.path.join(model_p
                         'time': int(time.time())
         }
         with open(log_path, 'a+') as f:
-            f.write("{time};{method};{trained_eps};{count};{sum};{mean};{commit}".format(**format_vars) + "\n")
+            f.write("{time};{method};{trained_eps};{count};{sum};{mean}".format(**format_vars) + "\n")
 
 def log_bench_eval_outcomes(outcomes, log_path, index, time, trained_eps = 0):
-    commit = subprocess.run(['git', 'describe', '--first-parent', '--always'], stdout=subprocess.PIPE).stdout.decode('utf-8').rstrip()
     for outcome in outcomes:
         scores = outcome[1]
         format_vars = { 'trained_eps': trained_eps,
@@ -128,28 +117,9 @@ def log_bench_eval_outcomes(outcomes, log_path, index, time, trained_eps = 0):
                         'mean': sum(scores) / len(scores),
                         'time': time,
                         'index': index,
-                        'commit': commit
         }
         with open(log_path, 'a+') as f:
-            f.write("{method};{count};{index};{time};{sum};{mean};{commit}".format(**format_vars) + "\n")
-
-def find_board_rep():
-    checkpoint_path = os.path.join(config['model_storage_path'], config['model'])
-    board_rep_path = os.path.join(checkpoint_path, "board_representation")
-    with open(board_rep_path, 'r') as f:
-        return f.read()
-
-
-def board_rep_file_exists():
-    checkpoint_path = os.path.join(config['model_storage_path'], config['model'])
-    board_rep_path = os.path.join(checkpoint_path, "board_representation")
-    return os.path.isfile(board_rep_path)
-
-def create_board_rep():
-    checkpoint_path = os.path.join(config['model_storage_path'], config['model'])
-    board_rep_path = os.path.join(checkpoint_path, "board_representation")
-    with open(board_rep_path, 'a+') as f:
-        f.write(config['board_representation'])
+            f.write("{method};{count};{index};{time};{sum};{mean}".format(**format_vars) + "\n")
 
 # Do actions specified by command-line
 if args.list_models:
@@ -173,22 +143,6 @@ if __name__ == "__main__":
 
     # Set up variables
     episode_count = config['episode_count']
-
-    if config['board_representation'] is None:
-        if board_rep_file_exists():
-            config['board_representation'] = find_board_rep()
-        else:
-            sys.stderr.write("Was not given a board_rep and was unable to find a board_rep file\n")
-            exit()
-    else:
-        if not board_rep_file_exists():
-            create_board_rep()
-        else:
-            if config['board_representation'] != find_board_rep():
-                sys.stderr.write("Board representation \"{given}\", does not match one in board_rep file, \"{board_rep}\"\n".
-                                 format(given = config['board_representation'], board_rep = find_board_rep()))
-                exit()
-
                   
     if args.train:
         network = Network(config, config['model'])
@@ -203,21 +157,15 @@ if __name__ == "__main__":
             if not config['train_perpetually']:
                 break
 
-    elif args.play:
-        network = Network(config, config['model'])
-        network.play_against_network()
-
+            
     elif args.eval:
         network = Network(config, config['model'])
-        network.restore_model()
-
-        for i in range(int(config['repeat_eval'])):
-            start_episode = network.episodes_trained
-            # Evaluation measures are described in `config`
-            outcomes = network.eval(config['episode_count'])
-            log_eval_outcomes(outcomes, trained_eps = start_episode)
-            # elif args.play:
-            # g.play(episodes = episode_count)
+        start_episode = network.episodes_trained
+        # Evaluation measures are described in `config`
+        outcomes = network.eval(config['episode_count'])
+        log_eval_outcomes(outcomes, trained_eps = start_episode)
+        # elif args.play:
+        # g.play(episodes = episode_count)
 
         
     elif args.bench_eval_scores:
@@ -239,7 +187,7 @@ if __name__ == "__main__":
         episode_counts = [25, 50, 100, 250, 500, 1000, 2500, 5000,
                           10000, 20000]
 
-        def do_eval():
+        def do_eval(sess):
             for eval_method in config['eval_methods']:
                 result_path = os.path.join(config['bench_storage_path'],
                                            eval_method) + "-{}.log".format(int(time.time()))
@@ -247,7 +195,8 @@ if __name__ == "__main__":
                     for i in range(sample_count):
                         start_time = time.time()
                         # Evaluation measure to be benchmarked are described in `config`
-                        outcomes = network.eval(episode_count = n)
+                        outcomes = network.eval(episode_count = n,
+                                                tf_session = sess)
                         time_diff = time.time() - start_time
                         log_bench_eval_outcomes(outcomes,
                                                 time = time_diff,
@@ -257,8 +206,8 @@ if __name__ == "__main__":
 
         # CMM: oh no
         import tensorflow as tf
-
-        network.restore_model()
-        do_eval()
+        with tf.Session() as session:
+            network.restore_model(session)
+            do_eval(session)
         
         

models/exp_decay_fat/checkpoint

@@ -0,0 +1,2 @@
+model_checkpoint_path: "model.ckpt-11397483"
+all_model_checkpoint_paths: "model.ckpt-11397483"

models/exp_decay_fat/episodes_trained

@@ -0,0 +1 @@
+202615

models/exp_decay_fat/logs/eval.log

@@ -0,0 +1,615 @@
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