def __init__(self, player, nb_rows, nb_cols, timelimit):
"""Create Dots and Boxes agent.
:param player: Player number, 1 or 2
:param nb_rows: Rows in grid
:param nb_cols: Columns in grid
:param timelimit: Maximum time allowed to send a next action.
"""
self.player = {player}
self.timelimit = timelimit
self.ended = False
self.board = Strings_board(nb_rows, nb_cols)
self.tree = MonteCarloSearchTree(nb_rows, nb_cols)
self.nodes = self.tree.tree['nodes']
self.odds = []
i = 0
self.moves = []
self.mctsmoves = 0
self.heuristicmoves = 0
self.mcts = True
self.times_for_move = []
while i < 120:
if (i % 2 != 0):
self.odds.append(i)
i += 1
def __init__(self, player, nb_rows, nb_cols, timelimit):
"""Create Dots and Boxes agent.
:param player: Player number, 1 or 2
:param nb_rows: Rows in grid
:param nb_cols: Columns in grid
:param timelimit: Maximum time allowed to send a next action.
"""
self.player = {player}
self.timelimit = timelimit
self.ended = False
self.board = Strings_board(nb_rows, nb_cols)
self.depth = 5
self.board2 = Coins_strings_board(nb_rows + 1, nb_cols + 1)
self.odds = []
self.evens = []
self.maxnumberofmoves = len(self.board.get_potential_moves())
self.numberofmovesdone = 0
i = 0
while i < 120:
if (i % 2 == 0):
self.evens.append(i)
else:
self.odds.append(i)
i += 1
def __init__(self, player, nb_rows, nb_cols, timelimit):
"""Create Dots and Boxes agent.
:param player: Player number, 1 or 2
:param nb_rows: Rows in grid
:param nb_cols: Columns in grid
:param timelimit: Maximum time allowed to send a next action.
"""
self.player = {player}
self.timelimit = timelimit
self.ended = False
self.board = Strings_board(nb_rows,nb_cols)
self.odds = []
i = 0
while i<120:
if(i%2!=0):
self.odds.append(i)
i += 1
class DotsAndBoxesAgent:
"""
A DotsAndBoxesAgent object should implement the following methods:
- __init__
- add_player
- register_action
- next_action
- end_game
This class does not necessarily use the best data structures for the
approach you want to use.
"""
def __init__(self, player, nb_rows, nb_cols, timelimit):
"""Create Dots and Boxes agent.
:param player: Player number, 1 or 2
:param nb_rows: Rows in grid
:param nb_cols: Columns in grid
:param timelimit: Maximum time allowed to send a next action.
"""
self.player = {player}
self.timelimit = timelimit
self.ended = False
self.board = Strings_board(nb_rows, nb_cols)
self.tree = MonteCarloSearchTree(nb_rows, nb_cols)
self.nodes = self.tree.tree['nodes']
self.odds = []
i = 0
self.moves = []
self.mctsmoves = 0
self.heuristicmoves = 0
self.mcts = True
self.times_for_move = []
while i < 120:
if (i % 2 != 0):
self.odds.append(i)
i += 1
def add_player(self, player):
"""Use the same agent for multiple players."""
self.player.add(player)
def register_action(self, row, column, orientation, player):
"""
Register action played in game.
:param row:
:param columns:
:param orientation: "v" or "h"
:param player: 1 or 2
"""
if (orientation == "h"):
y = column
x = row * 2
else:
y = column
x = self.odds[row]
self.board.fill_line(x, y)
self.moves.append(
str(row) + "," + str(column) + "," + str(orientation))
node = self.tree.fill_line(
self.nodes,
str(row) + "," + str(column) + "," + str(orientation))
if node != False:
self.nodes = node['children']
def next_action(self):
"""Return the next action this agent wants to perform.
:return: (row, column, orientation)
"""
start_time = time.time()
free_lines = self.board.get_potential_moves()
if len(free_lines) == 0:
# Board full
return None
signal.alarm(self.timelimit)
try:
(s, value) = self.tree.get_best_move_for_set(self.moves.copy())
if not isinstance(s, str) or self.mcts == False or s in self.moves:
(a, b) = heuristics.find_good_move(self.board)
signal.alarm(0)
self.mcts = False
if a % 2 == 0:
o = "h"
c = b
r = int(a / 2)
else:
o = "v"
c = b
r = self.odds.index(a)
self.heuristicmoves += 1
elapsed_time = time.time() - start_time
self.times_for_move.append(elapsed_time)
return r, c, o
else:
signal.alarm(0)
self.mctsmoves += 1
r, c, o = s.split(",")
elapsed_time = time.time() - start_time
self.times_for_move.append(elapsed_time)
return r, c, o
except TimeoutException:
(a, b) = heuristics.find_good_move(self.board)
signal.alarm(0)
self.mcts = False
if a % 2 == 0:
o = "h"
c = b
r = int(a / 2)
else:
o = "v"
c = b
r = self.odds.index(a)
self.heuristicmoves += 1
elapsed_time = time.time() - start_time
self.times_for_move.append(elapsed_time)
return r, c, o
def end_game(self):
time = 0
for t in self.times_for_move:
time += t
print("avg time v3 =", int(time) / len(self.times_for_move))
self.ended = True
class DotsAndBoxesAgent:
"""
A DotsAndBoxesAgent object should implement the following methods:
- __init__
- add_player
- register_action
- next_action
- end_game
This class does not necessarily use the best data structures for the
approach you want to use.
"""
def __init__(self, player, nb_rows, nb_cols, timelimit):
"""Create Dots and Boxes agent.
:param player: Player number, 1 or 2
:param nb_rows: Rows in grid
:param nb_cols: Columns in grid
:param timelimit: Maximum time allowed to send a next action.
"""
self.player = {player}
self.timelimit = timelimit
self.ended = False
self.board = Strings_board(nb_rows, nb_cols)
self.times_for_move = []
self.odds = []
i = 0
while i < 120:
if (i % 2 != 0):
self.odds.append(i)
i += 1
def add_player(self, player):
"""Use the same agent for multiple players."""
self.player.add(player)
def register_action(self, row, column, orientation, player):
"""
Register action played in game.
:param row:
:param columns:
:param orientation: "v" or "h"
:param player: 1 or 2
"""
if (orientation == "h"):
y = column
x = row * 2
else:
y = column
x = self.odds[row]
self.board.fill_line(x, y)
def next_action(self):
"""Return the next action this agent wants to perform.
:return: (row, column, orientation)
"""
start_time = time.time()
free_lines = self.board.get_potential_moves()
if len(free_lines) == 0:
# Board full
return None
(a, b) = heuristics.find_good_move(self.board)
if a % 2 == 0:
o = "h"
c = b
r = int(a / 2)
else:
o = "v"
c = b
r = self.odds.index(a)
elapsed_time = time.time() - start_time
self.times_for_move.append(elapsed_time)
return r, c, o
def end_game(self):
time = 0
for t in self.times_for_move:
time += t
print("avg time v3 =", int(time) / len(self.times_for_move))
self.ended = True
class DotsAndBoxesAgent:
"""
A DotsAndBoxesAgent object should implement the following methods:
- __init__
- add_player
- register_action
- next_action
- end_game
This class does not necessarily use the best data structures for the
approach you want to use.
"""
def __init__(self, player, nb_rows, nb_cols, timelimit):
"""Create Dots and Boxes agent.
:param player: Player number, 1 or 2
:param nb_rows: Rows in grid
:param nb_cols: Columns in grid
:param timelimit: Maximum time allowed to send a next action.
"""
self.player = {player}
self.timelimit = timelimit
self.ended = False
self.board = Strings_board(nb_rows,nb_cols)
self.tree = MonteCarloSearchTree(nb_rows,nb_cols)
self.nodes = self.tree.tree['nodes']
self.odds = []
i = 0
self.moves = []
self.mctsmoves = 0
self.heuristicmoves = 0
self.mcts = True
self.shouldnotstartwith = []
while i<120:
if(i%2!=0):
self.odds.append(i)
i += 1
def add_player(self, player):
"""Use the same agent for multiple players."""
self.player.add(player)
def register_action(self, row, column, orientation, player):
"""
Register action played in game.
:param row:
:param columns:
:param orientation: "v" or "h"
:param player: 1 or 2
"""
if(orientation == "h"):
y = column
x = row*2
else:
y = column
x = self.odds[row]
self.board.fill_line(x,y)
self.moves.append(str(row)+","+str(column)+","+str(orientation))
print(self.moves)
# node = self.tree.fill_line(self.nodes,str(row)+","+str(column)+","+str(orientation))
# if node != False:
# self.nodes = node['children']
def next_action(self):
"""Return the next action this agent wants to perform.
:return: (row, column, orientation)
"""
free_lines = self.board.get_potential_moves()
if len(free_lines) == 0:
# Board full
return None
move = False
if self.mcts:
value = 0
for l in it.permutations(self.moves, len(self.moves)):
go = True
for seq in self.shouldnotstartwith:
li = list(l)
if li[:len(seq)] == seq:
go = False
break
if go:
(newmove,rate) = self.tree.get_best_move_for_set(list(l).copy())
if rate > value and newmove not in self.moves:
value = rate
move = newmove
self.mctsmoves += 1
r,c,o = move.split(",")
return r,c,o
else:
self.shouldnotstartwith.append(list(l))
print("SHOULDNTO",self.shouldnotstartwith)
print("MCTS POWER")
if not isinstance(move, str) or self.mcts == False:
(a,b) = heuristics.find_good_move(self.board)
self.mcts = False
if a%2==0:
o = "h"
c = b
r = int(a/2)
else:
o = "v"
c = b
r = self.odds.index(a)
self.heuristicmoves += 1
return r, c, o
def end_game(self):
print("HEURISTIC MOVES:",self.heuristicmoves)
print("MCTS MOVES:",self.mctsmoves)
self.ended = True
class DotsAndBoxesAgent:
"""
A DotsAndBoxesAgent object should implement the following methods:
- __init__
- add_player
- register_action
- next_action
- end_game
This class does not necessarily use the best data structures for the
approach you want to use.
"""
def __init__(self, player, nb_rows, nb_cols, timelimit):
"""Create Dots and Boxes agent.
:param player: Player number, 1 or 2
:param nb_rows: Rows in grid
:param nb_cols: Columns in grid
:param timelimit: Maximum time allowed to send a next action.
"""
self.player = {player}
self.timelimit = timelimit
self.ended = False
self.board = Strings_board(nb_rows, nb_cols)
self.depth = 5
self.board2 = Coins_strings_board(nb_rows + 1, nb_cols + 1)
self.odds = []
self.evens = []
self.maxnumberofmoves = len(self.board.get_potential_moves())
self.numberofmovesdone = 0
i = 0
while i < 120:
if (i % 2 == 0):
self.evens.append(i)
else:
self.odds.append(i)
i += 1
def add_player(self, player):
"""Use the same agent for multiple players."""
self.player.add(player)
def register_action(self, row, column, orientation, player):
"""
Register action played in game.
:param row:
:param columns:
:param orientation: "v" or "h"
:param player: 1 or 2
"""
if (orientation == "h"):
y = column
x = row * 2
else:
y = column
x = self.odds[row]
self.board.fill_line(x, y)
if (orientation == "h"):
a = self.evens[row]
b = self.odds[column]
else:
a = self.odds[row]
b = self.evens[column]
self.board2.fill_line(a, b, player)
self.numberofmovesdone += 1
def next_action(self):
"""Return the next action this agent wants to perform.
:return: (row, column, orientation)
"""
free_lines = self.board.get_potential_moves()
if len(free_lines) == 0:
# Board full
return None
# Start the timer. Once 5 seconds are over, a SIGALRM signal is sent.
# This try/except loop ensures that
# you'll catch TimeoutException when it's sent.
if (self.numberofmovesdone / self.maxnumberofmoves * 100 < 60):
(a, b) = heuristics.heuristic(self.board)
signal.alarm(0)
if a % 2 == 0:
o = "h"
c = b
r = int(a / 2)
else:
o = "v"
c = b
r = self.odds.index(a)
return r, c, o
signal.alarm(self.timelimit)
try:
(a, b, score) = abv1.alphabeta(self.board2,
10,
player=list(self.player)[0])
signal.alarm(0)
self.depth += 1
if a % 2 == 0:
x = self.odds.index(b)
y = self.evens.index(a)
return (y, x, "h")
else:
y = self.odds.index(a)
x = self.evens.index(b)
return (y, x, "v")
except TimeoutException:
self.depth -= 1
(a, b) = heuristics.heuristic(self.board)
signal.alarm(0)
if a % 2 == 0:
o = "h"
c = b
r = int(a / 2)
else:
o = "v"
c = b
r = self.odds.index(a)
return r, c, o
def end_game(self):
self.ended = True
def train(nb_rows, nb_cols):
board = Strings_board(nb_rows, nb_cols)
edges = []
for i, row in enumerate(board.board):
for j, val in enumerate(row):
board.board[i][j] = True
edges.append((i, j))
for m in range(nb_rows * nb_cols):
board_states = combinations(edges, m)
if m == 0: #end states
board_num = str(qlearning.board2num(board.board))
max_score = (nb_rows * nb_cols)
Qedge = [dict()]
Q = [[dict() for i in range(max_score + 1)]]
for score in range(max_score + 1):
Q[m][score][board_num] = RW * (score - (max_score // 2))
else:
# initiate row in Q-value lookup table
Q.append(
[dict() for i in range(max_score - int(np.floor(m // 4)))])
Qedge.append(dict())
for board_state in board_states:
# construct board from board state
for edge in board_state:
i, j = edge
board.board[i][j] = False
# find out how many squares are already filled
total_score = max_score - len(board.get_potential_moves())
# find the best move - it must be the same, regardless of score
# we will consider it to be total_score
potential_moves = []
for i, row in enumerate(board.board):
for j, val in enumerate(row):
if val == False:
gain = board.check_surrounding_squares((i, j), 3)
# move remembers edge inserted and score gain,
potential_moves.append(((i, j), gain))
# map potential_moves on Q-values
qmax = -np.inf
board_num = qlearning.board2num(board.board)
for move in potential_moves:
edge, gain = move
edge_num = qlearning.edge2num(edge)
if gain:
qval = gamma * Q[m - 1][total_score +
gain][str(board_num +
edge_num)]
else:
qval = -gamma * Q[m - 1][0][str(board_num + edge_num)]
if qmax < qval:
qmax = qval
best_edge = edge
# insert Q-values
for score_state in range(total_score + 1):
Q[m][score_state][str(
board_num)] = qmax - RW * (total_score - score_state)
# insert best move
Qedge[m][str(board_num)] = best_edge
for edge in board_state:
i, j = edge
board.board[i][j] = True
return (Q, Qedge)