def get_sucessors(self):
# TODO: consider accuracy, crits, status, move effects, etc. when choosing sucessors
# TODO: rearrange child order for optimization
sucessors = []
#import pdb; pdb.set_trace()
for a in Action.get_all():
for b in Action.get_all():
if self.are_actions_valid(a, b):
sucessors.append((a, b, self.mutate(a, b)))
return sucessors
def get_sucessors(self):
# TODO: consider accuracy, crits, status, move effects, etc. when choosing sucessors
# TODO: rearrange child order for optimization
sucessors = []
#import pdb; pdb.set_trace()
for a in Action.get_all():
for b in Action.get_all():
if self.are_actions_valid(a, b):
sucessors.append((a, b, self.mutate(a, b)))
return sucessors
def get_next_action(self):
build_game_tree(self)
if CONF['debug_level'] >= 2:
print('Current State: ' + str(self))
for c in self.children:
print(
'Team1 Move: %s,\nTeam2 Move: %s,\nNew State Value: %f,\nNew State {\n%s\n}'
% (Action.to_str(c[0]), Action.to_str(
c[1]), c[2].worst_child_val, str(c[2])))
# choose an action: get the worst value from each possible action of the opponent and group
# it by move. Then, choose the move with the highest value.
worst_actions_group_by_a = []
for a in Action.get_all():
l = []
for c in self.children:
if c[0] == a:
l.append((c[2].state.value(), c[2].state))
worst_actions_group_by_a.append(
min([c[0] for c in l]) if l else -1.0)
best_action = 0
max_val = worst_actions_group_by_a[0]
for i in range(1, len(worst_actions_group_by_a)):
if max_val < worst_actions_group_by_a[i]:
best_action = i
max_val = worst_actions_group_by_a[i]
return best_action
def get_next_action(self):
build_game_tree(self)
if CONF['debug_level'] >= 1:
print('Current State: ' + str(self))
for c in self.children:
print('Team1 Move: %s,\nTeam2 Move: %s,\nNew State Value: %f,\nNew State {\n%s\n}' % (
Action.to_str(c[0]),
Action.to_str(c[1]),
c[2].worst_child_val,
str(c[2])))
# choose an action: get the worst value from each possible action of the opponent and group
# it by move. Then, choose the move with the highest value.
worst_actions_group_by_a = []
for a in Action.get_all():
l = []
for c in self.children:
if c[0] == a:
l.append((c[2].state.value(), c[2].state))
worst_actions_group_by_a.append(
min([c[0] for c in l]) if l else -1.0
)
best_action = 0
max_val = worst_actions_group_by_a[0]
for i in range(1, len(worst_actions_group_by_a)):
if max_val < worst_actions_group_by_a[i]:
best_action = i
max_val = worst_actions_group_by_a[i]
return best_action
def random_get_enemy_action(game_tree):
valid_actions = []
for a in Action.get_all():
if game_tree.state.is_action_valid(a, game_tree.state.team2):
valid_actions.append(a)
return random.choice(valid_actions)
def random_get_enemy_action(game_tree):
valid_actions = []
for a in Action.get_all():
if game_tree.state.is_action_valid(a, game_tree.state.team2):
valid_actions.append(a)
return random.choice(valid_actions)
