def test_select_node():
board = cm.initialize_game_state()
child_board = cm.initialize_game_state()
child_board[0, 0] = cm.PLAYER1
current_node = node.mcts_node(state=board)
child_node = node.mcts_node(state=child_board, parent=current_node)
current_node.open_moves = [0, 3, 4]
current_node.children = [child_node]
selected_node = agent.select_node(current_node)
# since it has unexpanded node, hence it will return the current node
assert selected_node == current_node
def test_explore_node():
board = cm.initialize_game_state()
current_node = node.mcts_node(state=board)
current_node.open_moves = [0, 3, 4]
explored_node = agent.explore_node(current_node)
# explore a new open node
assert len(current_node.open_moves) == 2
assert explored_node != current_node
def test_child_selection():
board = cm.initialize_game_state()
child_board01 = board.copy()
child_board01[0, 0] = cm.PLAYER1
child_board02 = board.copy()
child_board02[0, 3] = cm.PLAYER1
child_node01 = node.mcts_node(state=child_board01, player=cm.PLAYER1)
child_node02 = node.mcts_node(state=child_board02, player=cm.PLAYER1)
current_node = node.mcts_node(state=board, player=cm.PLAYER1)
current_node.total_visits = 100
child_node01.total_visits = 50
child_node02.total_visits = 40
child_node01.num_wins = 35
child_node02.num_wins = 25
children_array = [child_node01, child_node02]
current_node.children = children_array
assert child_node01.__eq__(current_node.select_next_node())
def test_get_open_moves_connected4():
board = cm.initialize_game_state()
board[0, 0] = cm.PLAYER1
board[0, 1] = cm.PLAYER1
board[0, 2] = cm.PLAYER1
board[0, 3] = cm.PLAYER1
current_node = node.mcts_node(state=board, player=cm.PLAYER1)
assert current_node.get_open_moves().shape[0] == 0
def test_expansion():
board = cm.initialize_game_state()
current_node = node.mcts_node(state=board, player=cm.PLAYER1)
current_node.total_visits = 100
current_node.open_moves = [0, 5]
assert len(current_node.open_moves) == 2
assert len(current_node.children) == 0
child_node = current_node.expand_node(5)
assert len(current_node.open_moves) == 1
assert len(current_node.children) == 1
assert current_node.open_moves[0] == 0
assert child_node.__eq__(current_node.children[0])
def MCTS(board: np.ndarray) -> cn.PlayerAction:
"""
The monte carlo tree search algorithm
:param board: The board against which to calculate the simulation
:return: Column in which player wants to make his move (chosen using MCTS)
"""
# initialise root node to start the algorithm
root = mcts_node.mcts_node(state=board, player=PLAYER)
global GLOBAL_TIME
# defining the limiting factor to get out of the loop
end = time.time() + GLOBAL_TIME
while time.time() < end:
# Start at the root node at each iteration
node = root
# Step 1: select_next_node - this selects a terminal node or an unexplored node
node = select_node(node)
# Step 2: Explore the node that is selected
node = explore_node(node)
# Step 3 : Simulation
win_game_flag, current_player = simulate_game(node)
# Step 4: Back propagation
back_propagation(node, win_game_flag, current_player)
max_score = - 10000000
selected_column = - 1
for child in root.children:
if cn.connected_four(child.state, child.player):
return child.move
else:
score = child.num_wins / child.total_visits
if score > max_score:
selected_column = child.move
max_score = score
return selected_column
def test_simulate_game():
board = cm.initialize_game_state()
current_node = node.mcts_node(state=board, player=cm.PLAYER1)
win, player = agent.simulate_game(current_node)
# simulated till a win is encountered
assert win
def test_back_propagation():
board = cm.initialize_game_state()
current_node = node.mcts_node(state=board)
agent.back_propagation(current_node, cm.PLAYER1, False)
assert current_node.total_visits == 1
def test_get_open_moves():
board = cm.initialize_game_state()
current_node = node.mcts_node(state=board, player=cm.PLAYER1)
assert current_node.get_open_moves().shape[0] == 7
def test_set_visit_win():
board = cm.initialize_game_state()
current_node = node.mcts_node(state=board, player=cm.PLAYER1)
current_node.set_visit_and_win(result=10)
assert current_node.num_wins == 10
assert current_node.total_visits == 1
