def search(self, state, depth, max_player):
"""Start the MiniMax algorithm.
:param state: The state to start from.
:param depth: The maximum allowed depth for the algorithm.
:param max_player: Whether this is a max node (True) or a min node (False).
:return: A tuple: (The min max algorithm value, The direction in case of max node or None in min mode)
"""
self.throw_exception_if_timeout(state)
if self.goal and self.goal(state, max_player):
return (self.utility(state, max_player), state.direction)
if depth == 0:
return (self.utility(state, max_player), state.direction)
childrens = self.succ(state, max_player)
if max_player:
currMax = State(None, None, None, None, None, None)
currMax.value = -np.inf
for c in childrens:
v = self.search(c, depth - 1, not max_player)
c.value = v[0]
currMax = max(currMax, c)
return (currMax.value, currMax.direction)
else:
currMin = State(None, None, None, None, None, None)
currMin.value = np.inf
for c in childrens:
v = self.search(c, depth - 1, not max_player)
c.value = v[0]
currMin = min(currMin, c)
return (currMin.value, currMin.direction)
def search(self,
state,
depth,
is_father_max,
alpha=ALPHA_VALUE_INIT,
beta=BETA_VALUE_INIT):
"""Start the AlphaBeta algorithm.
:param state: The state to start from.
:param depth: The maximum allowed depth for the algorithm.
:param is_father_max: Whether this is a max node (True) or a min node (False).
:param alpha: alpha value
:param beta: beta value
:return: A tuple: (The min max algorithm value, The direction in case of max node or None in min mode)
"""
self.throw_exception_if_timeout(state)
if self.goal and self.goal(state):
return (self.utility(state, is_father_max), state.direction)
if depth == 0:
return (self.utility(state, is_father_max), state.direction)
children = self.succ(state, not is_father_max)
if is_father_max:
currMax = State(None, None, None, None, None, None, None, None,
None, None, None)
currMax.value = -np.inf
for c in children:
v = self.search(c, depth - 1, not is_father_max, alpha, beta)
c.value = v[0]
currMax = max(currMax, c)
alpha = max(currMax.value, alpha)
if currMax.value >= beta:
return np.inf, currMax.direction
# self.restore_father(is_father_max, state, children)
return currMax.value, currMax.direction
else:
currMin = State(None, None, None, None, None, None, None, None,
None, None, None)
currMin.value = np.inf
for c in children:
v = self.search(c, depth - 1, not is_father_max, alpha, beta)
c.value = v[0]
currMin = min(currMin, c)
beta = min(currMin.value, beta)
if currMin.value <= alpha:
return -np.inf, currMin.direction
# self.restore_father(is_father_max, state, children)
return (currMin.value, currMin.direction)