Test commit

2 files changed, 92 insertions, 56 deletions

diff --git a/Algorithm.py b/Algorithm.py
index 5a2e4f8..99488a8 100644
--- a/Algorithm.py
+++ b/Algorithm.py
@@ -1,3 +1,11 @@
+# import MCTS

+import math

+from copy import deepcopy

+from time import clock

+from random import choice

+

+from GameState import GameState

+

 class Algo: # A class for defining algorithms used (minimax and alpha-beta pruning)

     

     def miniMax(State, Ply_num): # Function for the minimax algorithm

@@ -66,53 +74,75 @@ class Algo: # A class for defining algorithms used (minimax and alpha-beta pruni
                 return Result

 

         return Minimum_Score

-class MCTS:

-    # main function for the Monte Carlo Tree Search

-

-    def monte_carlo_tree_search(root):

-

-	    while resources_left(time, computational power):

-		    leaf = traverse(root)

-		    simulation_result = rollout(leaf)

-		    backpropagate(leaf, simulation_result)

-

-	    return best_child(root)

-

-# function for node traversal

-

-

-    def traverse(node):

-	    while fully_expanded(node):

-		    node = best_uct(node)

-

-	    # in case no children are present / node is terminal

-	    return pick_univisted(node.children) or node

-

-# function for the result of the simulation

-

-

-    def rollout(node):

-	    while non_terminal(node):

-		    node = rollout_policy(node)

-	    return result(node)

-

-# function for randomly selecting a child node

-

-

-    def rollout_policy(node):

-	    return pick_random(node.children)

-

-# function for backpropagation

-

-

-    def backpropagate(node, result):

-	    if is_root(node) return

-	    node.stats = update_stats(node, result)

-	    backpropagate(node.parent)

-

-# function for selecting the best child

-# node with highest number of visits

-

 

-    def best_child(node):

-	    #pick child with highest number of visits

+# class MCTSNode(object):

+#     """Monte Carlo Tree Node.

+#     Each node encapsulates a particular game state, the moves that

+#     are possible from that state and the strategic information accumulated

+#     by the tree search as it progressively samples the game space.

+#     """

+

+#     def __init__(self, state, parent=None, move=None):

+#         self.parent = parent

+#         self.move = move

+#         self.state = state

+

+#         self.plays = 0

+#         self.score = 0

+

+#         self.pending_moves = state.get_moves()

+#         self.children = []

+

+#     def select_child_ucb(self):

+#         # Note that each node's plays count is equal

+#         # to the sum of its children's plays

+#         def ucb(child):

+#             win_ratio = child.score / child.plays \

+#                         + math.sqrt(2 * math.log(self.plays) / child.plays)

+#             return win_ratio

+

+#         return max(self.children, key=ucb)

+

+#     def expand_move(self, move):

+#         self.pending_moves.remove(move)  # raises KeyError

+

+#         child_state = deepcopy(self.state)

+#         child_state.play_move(move)

+

+#         child = MCTSNode(state=child_state, parent=self, move=move)

+#         self.children.append(child)

+#         return child

+

+#     def get_score(self, result):

+#         # return result

+#         if result == 0.5:

+#             return result

+

+#         if self.state.player == 2:

+#             if self.state.next_turn_player == result:

+#                 return 0.0

+#             else:

+#                 return 1.0

+#         else:

+#             if self.state.next_turn_player == result:

+#                 return 1.0

+#             else:

+#                 return 0.0

+

+#         if self.state.next_turn_player == result:

+#             return 0.0

+#         else:

+#             return 1.0

+

+#     def __repr__(self):

+#         s = 'ROOT\n' if self.parent is None else ''

+

+#         children_moves = [c.move for c in self.children]

+

+#         s += """Score ratio: {score} / {plays}

+# Pending moves: {pending_moves}

+# Children's moves: {children_moves}

+# State:

+# {state}\n""".format(children_moves=children_moves, **self.__dict__)

+

+#         return s        
\ No newline at end of file
diff --git a/DotsNBoxes.py b/DotsNBoxes.py
index ebafcc2..14cdd2a 100644
--- a/DotsNBoxes.py
+++ b/DotsNBoxes.py
@@ -3,6 +3,7 @@ import collections
 from Algorithm import *

 from Board import *

 from Nodes import *

+import MCTS

 

 

 class DotsNBoxes: # A class for managing the moves made by the human and the computer

@@ -15,19 +16,21 @@ class DotsNBoxes: # A class for managing the moves made by the human and the com
 

     def Human(self): # Defining the Human player and his actions/Choices

         self.State.Draw()

-

-       # HumanX = int(input("Please enter the 'X' coordinate of your choice (an integer such as 4): "))

+        

+        # HumanX = int(input("Please enter the 'X' coordinate of your choice (an integer such as 4): "))

         # HumanY = int(input("Please enter the 'Y' coordinate of your choice (an integer such as 4): "))

-       # if (HumanX, HumanY) not in self.State.children:

+        # if (HumanX, HumanY) not in self.State.children:

         #    self.State.Make(HumanX, HumanY, False)

-          #  self.State = self.State.children[(HumanX, HumanY)]

-       # else:

         #    self.State = self.State.children[(HumanX, HumanY)]

+           

+        # else:

+        #    self.State = self.State.children[(HumanX, HumanY)]

+        

         move = Algo.miniMax(self.State, 2)

 

         self.State = self.State.children[(move[0], move[1])]

 

-        print("AI selected the following coordinates to play:\n" + "(" ,str(move[0]), ", " + str(move[1]), ")", end = "\n\n")

+        print("AI1 selected the following coordinates to play:\n" + "(" ,str(move[0]), ", " + str(move[1]), ")", end = "\n\n")

 

         print("Current Score =====>> Your Score - AI Score = " + str(self.State.CurrentScore), end = "\n\n\n")

 

@@ -42,10 +45,13 @@ class DotsNBoxes: # A class for managing the moves made by the human and the com
     def Computer(self): # Defining the Computer player and its actions/Choices

         self.State.Draw()

 

+        # Temporarily commented out. TODO hardcore MCTS into working then alternate comments lol 

         move = Algo.miniMax(self.State, 3)

-

         self.State = self.State.children[(move[0], move[1])]

 

+        # move = MCTS.MCTSGameController() # check what MCTSGameController returns?

+        

+

         print("AI2 selected the following coordinates to play:\n" + "(" ,str(move[0]), ", " + str(move[1]), ")", end = "\n\n")

 

         print("Current Score =====>> Your Score - AI Score = " + str(self.State.CurrentScore), end = "\n\n\n")