def _traverse_from(self, grid: Grid, current_color: Color) -> bool:
self._nodes_for_backprop.append((grid.state, current_color))
node_info: NodeInfo = self._tree[(grid.state, current_color)]
if not node_info.is_leaf:
move = self._select_best_child_ucb(grid, current_color)
return self._traverse_from(
grid.grid_after_move(current_color, move),
Color(1 - current_color))
elif len(grid.available_moves) == 0:
return False
elif node_info.visits == 0:
return self._rollout_from(grid, current_color)
else:
self._tree[(grid.state,
current_color)] = NodeInfo(wins=node_info.wins,
visits=node_info.visits,
is_leaf=False)
for move in grid.available_moves:
new_grid = grid.grid_after_move(current_color, move)
if (new_grid.state,
Color(1 - current_color)) not in self._tree.keys():
self._tree[(new_grid.state,
1 - current_color)] = NodeInfo(wins=0,
visits=0,
is_leaf=True)
move = random.choice(grid.available_moves)
return self._traverse_from(
grid.grid_after_move(current_color, move),
Color(1 - current_color))
def _minmax(self, grid: Grid, depth: int, alpha: int, beta: int,
color: Color, last_move: Union[int, None]) -> Tuple[int, int]:
"""Returns (best available value, move towards best value)"""
computed = self._transposition_table.get(grid.state, None)
if computed is not None and computed[0] >= depth: return computed[1:]
if self._deadline.is_set() or depth == 0 or \
(last_move is not None and self._judge.is_over_after_move_in_col(grid.state, last_move)):
return self._evaluate(grid.state, self._judge), 0
value = -INF if color == MAX_COLOR else INF
best_move = None
for move in grid.available_moves:
child_value, _ = self._minmax(grid.grid_after_move(color, move),
depth - 1, alpha, beta,
Color(1 - color), move)
if color == MAX_COLOR and child_value > value:
best_move = move
value = child_value
alpha = max(alpha, value)
elif color == MIN_COLOR and child_value < value:
best_move = move
value = child_value
beta = min(beta, value)
if alpha >= beta: break
self._transposition_table[grid.state] = (depth, value, best_move)
return value, best_move
def _rollout_from(self,
grid: Grid,
color: Color,
last_col: Union[int, None] = None) -> bool:
if len(grid.available_moves) == 0 or (last_col is None and self._judge.is_over(grid.state)) or \
(last_col is not None and self._judge.is_over_after_move_in_col(grid.state, last_col)):
return color != self._color
move = random.choice(grid.available_moves)
has_won = self._rollout_from(grid.grid_after_move(color, move),
Color(1 - color), move)
return has_won
def _select_best_child(self, parent: Grid, map_node_info: callable,
current_color: Color) -> int:
"""map_node_info should take parent's node_info and child's and return a positive float"""
assert len(parent.available_moves) > 0, 'A leaf actually'
parent_info = self._tree[(parent.state, current_color)]
best_move, best_result = None, -1
for move in parent.available_moves:
child_info = self._tree[(parent.grid_after_move(
current_color, move).state, Color(1 - current_color))]
temp_result = map_node_info(parent_info, child_info)
if temp_result > best_result:
best_result, best_move = temp_result, move
return best_move
def _finishing_move_in(self, grid: Grid) -> Union[int, None]:
for move in grid.available_moves:
after_move = grid.grid_after_move(self._color, move)
if self._judge.is_over_after_move_in_col(after_move.state, move):
return move