aboutsummaryrefslogtreecommitdiffstats
diff options
context:
space:
mode:
authorMatt Strapp <strap012@umn.edu>2021-04-26 13:07:08 -0500
committerMatt Strapp <strap012@umn.edu>2021-04-26 15:03:24 -0500
commit136458d96e5f0ae683d42d49ec4c85124d255b22 (patch)
tree438c299b8b869df29e43e7c0ce3ee917e5ad0f98
parentStart work on AB (diff)
downloadcsci4511w-136458d96e5f0ae683d42d49ec4c85124d255b22.tar
csci4511w-136458d96e5f0ae683d42d49ec4c85124d255b22.tar.gz
csci4511w-136458d96e5f0ae683d42d49ec4c85124d255b22.tar.bz2
csci4511w-136458d96e5f0ae683d42d49ec4c85124d255b22.tar.lz
csci4511w-136458d96e5f0ae683d42d49ec4c85124d255b22.tar.xz
csci4511w-136458d96e5f0ae683d42d49ec4c85124d255b22.tar.zst
csci4511w-136458d96e5f0ae683d42d49ec4c85124d255b22.zip
Test commit
Diffstat (limited to '')
-rw-r--r--python/alphaBeta.py253
1 files changed, 148 insertions, 105 deletions
diff --git a/python/alphaBeta.py b/python/alphaBeta.py
index 8e041fe..7279368 100644
--- a/python/alphaBeta.py
+++ b/python/alphaBeta.py
@@ -1,105 +1,148 @@
-from GameState import GameState
-class GameController(object):
- def get_next_move(self, state):
- # when you get a new move, it is assumed that the game is not ended yet
- assert state.get_moves()
-
-
-def alpha_beta(node, alpha, beta):
-
- # Based on https://en.wikipedia.org/wiki/Alpha%E2%80%93beta_pruning#Pseudocode
- # node needs to support three operations: isTerminal(), value(), getChildren(), maximizingPlayer()
-
- if node.isTerminal():
- return node.value()
-
- if node.maximizingPlayer():
-
- v = float("-inf")
- for child in node.getChildren():
-
- v = max(v, alpha_beta(child, alpha, beta))
- alpha = max(alpha, v)
- if beta <= alpha:
- break
-
- else:
-
- v = float("inf")
- for child in node.getChildren():
-
- v = min(v, alpha_beta(child, alpha, beta))
- beta = min(beta, v)
- if beta <= alpha:
- break
-
- return v
-
-
-# A class for defining algorithms used (minimax and alpha-beta pruning)
-class AlphaBeta:
-
- def miniMax(State, Ply_num): # Function for the minimax algorithm
-
- for i in range(State.Current.dimY):
- for j in range(State.Current.dimX):
- if State.Current.Mat[i][j] == ' ' and (j, i) not in State.children:
- State.Make(j, i, True)
- if Ply_num < 2:
- return (i, j)
-
- Minimum_Score = 1000
- i = 0
-
- j = 0
- for k, z in State.children.items():
- Result = Algo.Maximum(z, Ply_num - 1, Minimum_Score)
- if Minimum_Score > Result:
- Minimum_Score = Result
- i = k[0]
- j = k[1]
-
- return (i, j)
-
- # Alpha-beta pruning function for taking care of Alpha values
- def Maximum(State, Ply_num, Alpha):
- if Ply_num == 0:
- return State.CurrentScore
-
- for i in range(State.Current.dimY):
- for j in range(State.Current.dimX):
- if State.Current.Mat[i][j] == ' ' and (j, i) not in State.children:
- State.Make(j, i, False)
-
- Maximum_Score = -1000
- i = 0
- j = 0
- for k, z in State.children.items():
- Result = Algo.Minimum(z, Ply_num - 1, Maximum_Score)
- if Maximum_Score < Result:
- Maximum_Score = Result
- if Result > Alpha:
- return Result
-
- return Maximum_Score
-
- def Minimum(State, Ply_num, Beta): # Alpha-beta pruning function for taking care of Beta values
- if Ply_num == 0:
- return State.CurrentScore
-
- for i in range(State.Current.dimY):
- for j in range(State.Current.dimX):
- if State.Current.Mat[i][j] == ' ' and (j, i) not in State.children:
- State.Make(j, i, True)
-
- Minimum_Score = 1000
- i = 0
- j = 0
- for k, z in State.children.items():
- Result = Algo.Maximum(z, Ply_num - 1, Minimum_Score)
- if Minimum_Score > Result:
- Minimum_Score = Result
- if Result < Beta:
- return Result
-
- return Minimum_Score
+# 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
+
+ for i in range(State.Current.dimY):
+ for j in range(State.Current.dimX):
+ if State.Current.Mat[i][j] == ' ' and (j, i) not in State.children:
+ State.Make(j, i, True)
+ if Ply_num < 2:
+ return (i, j)
+
+ Minimum_Score = 1000
+ i = 0
+
+
+ j = 0
+ for k, z in State.children.items():
+ Result = Algo.Maximum(z, Ply_num - 1, Minimum_Score)
+ if Minimum_Score > Result:
+ Minimum_Score = Result
+ i = k[0]
+ j = k[1]
+
+ return (i, j)
+
+
+ def Maximum(State, Ply_num, Alpha): # Alpha-beta pruning function for taking care of Alpha values
+ if Ply_num == 0:
+ return State.CurrentScore
+
+ for i in range(State.Current.dimY):
+ for j in range(State.Current.dimX):
+ if State.Current.Mat[i][j] == ' ' and (j, i) not in State.children:
+ State.Make(j, i, False)
+
+ Maximum_Score = -1000
+ i = 0
+ j = 0
+ for k, z in State.children.items():
+ Result = Algo.Minimum(z, Ply_num - 1, Maximum_Score)
+ if Maximum_Score < Result:
+ Maximum_Score = Result
+ if Result > Alpha:
+ return Result
+
+ return Maximum_Score
+
+
+ def Minimum(State, Ply_num, Beta): # Alpha-beta pruning function for taking care of Beta values
+ if Ply_num == 0:
+ return State.CurrentScore
+
+ for i in range(State.Current.dimY):
+ for j in range(State.Current.dimX):
+ if State.Current.Mat[i][j] == ' ' and (j, i) not in State.children:
+ State.Make(j, i, True)
+
+ Minimum_Score = 1000
+ i = 0
+ j = 0
+ for k, z in State.children.items():
+ Result = Algo.Maximum(z, Ply_num - 1, Minimum_Score)
+ if Minimum_Score > Result:
+ Minimum_Score = Result
+ if Result < Beta:
+ return Result
+
+ return Minimum_Score
+
+# 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
generated by cgit v1.2.3 (git 2.51.0) at 2025-11-04 15:21:06 +0000