class GBRTTrainer(): #trains using Bayesian global optimization with GBRT against random play
def __init__(self,max_moves,ai_config,opponent_config,param_ranges,n_iter,n_games,init_params,n_batches = 1):
self.ai_config = ai_config
self.opponent_config = opponent_config
self.param_ranges = param_ranges
self.n_iter = n_iter
self.n_games = n_games
self.max_moves = max_moves
self.engine = Engine(self.max_moves,self.ai_config, self.opponent_config)
self.params = init_params
self.n_batches = n_batches
self.score_history = []
self.the_good_ones = []
def objective(self,params):
self.ai_config[Settings.AI_PARAMS.value] = params
wins = 0
for i in range(self.n_games):
self.engine.restart(self.max_moves,self.ai_config,self.opponent_config)
winner = self.engine.run()
if winner == 1:
wins += 1.0
elif winner == 0:
wins += 0.5
print wins / self.n_games
self.score_history.append(wins / self.n_games)
if(wins / self.n_games) >= .99:
#print(params)
self.the_good_ones.append(params)
#return 1 - (wins / self.n_games)
return - self.engine.get_value(1, 100, -1, 500)
#res = 0
#for i in range(self.n_games):
# self.engine.restart(self.max_moves,self.ai_config,self.opponent_config)
# self.engine.run()
# val = self.engine.get_value(1,100,-1,500)
# res += val
#print res
#self.score_history.append(res)
#return -res
def train(self):
opt = Optimizer(self.param_ranges,"GP")
r = opt.run(self.objective,n_iter=self.n_iter)
self.params = r.x
#print("trained paramaters are: ")
#print(r.x)
def objective_batchwise(self,params):
full_params = self.params[:self.training_param_range[0]] + params + self.params[self.training_param_range[1]:]
self.ai_config[Settings.AI_PARAMS.value] = full_params
wins = 0
for i in range(self.n_games):
self.engine.restart(self.max_moves,self.ai_config,self.opponent_config)
self.engine.run()
val = self.engine.get_value(1,100,-1,500)
res += val
print res
return -res
def train_batchwise(self):
opt = Optimizer(self.param_ranges,"GP")
for batch in range(self.n_batches):
print("starting a batch")
for start in range(10):
print("starting a round of optimization")
self.training_param_range = [start*10, start*10 + 10]
opt = Optimizer(self.param_ranges[start*10:start*10 + 10],"GP")
r = opt.run(self.objective,n_iter=self.n_iter)
self.params[start*10:start*10 + 10] = r.x
def evaluate(self):
wins = 0.0
for i in range(self.n_games):
self.engine.restart(self.max_moves,self.ai_config,self.opponent_config)
winner = self.engine.run()
if winner == 1:
wins += 1.0
elif winner == 0:
wins += 0.5
print winner
return wins / (self.n_games)
class Tournement:
def __init__(self, config, max_moves):
self.ai_configs = []
for filename in config[AI_FIELD][AIType.REACHABLE.value]:
self.ai_configs += read_file_into_configs(filename,
reachable_config)
for filename in config[AI_FIELD][AIType.MODIFIED_REACHABLE.value]:
self.ai_configs += read_file_into_configs(filename,
mod_reachable_config)
for filename in config[AI_FIELD][AIType.PIECE_BASED_ADD.value]:
self.ai_configs += read_file_into_configs(filename,
piecebased_config_add)
for filename in config[AI_FIELD][AIType.PIECE_BASED_MUL.value]:
self.ai_configs += read_file_into_configs(filename,
piecebased_config_mul)
self.start_configs = []
for filename in config[START_FIELD]:
self.start_configs += read_file_into_configs(
filename, flexible_start_config)
self.engine = Engine(max_moves, None, None)
self.max_moves = max_moves
def play_against(self, config1, config2, n_games=5):
player1_wins = 0
player2_wins = 0
for i in range(n_games):
self.engine.restart(self.max_moves, config1, config2)
winner = self.engine.run()
if winner == 1:
player1_wins += 1
elif winner == 2:
player2_wins += 1
if player1_wins > player2_wins:
return 1
elif player1_wins < player2_wins:
return 2
else:
return 0
def playoff_helper(self, param_lst, num):
this_round = param_lst
next_round = []
i = 1
while len(this_round) > num:
print("round {}, {} configurations left".format(
i, len(this_round)))
i += 1
random.shuffle(this_round)
for i in range(int(len(this_round) / 2)):
config1 = param_lst[i]
config2 = param_lst[i + 1]
# do a playoff and the winner goes to the next round
winner = self.play_against(config1, config2)
if winner == 1:
next_round.append(config1)
elif winner == 2:
next_round.append(config2)
else:
#in the event of a tie, play again with twice the number of
#moves to try to determine the winner
self.engine.restart(2 * self.max_moves, config1, config2)
winner = self.engine.run()
if winner == 1:
next_round.append(config1)
elif winner == 2:
next_round.append(config2)
else:
#double tie indicates the configs are about the same
#so its safe to just randomly pick one to move on to
#the next round
if random.random() < 0.5:
next_round.append(config1)
else:
next_round.append(config2)
if (len(this_round) % 2 != 0):
next_round.append(this_round[-1])
this_round = next_round
next_round = []
return this_round
def playoff(self, num=1):
print('finding the best ai configurations')
best_ai = self.playoff_helper(self.ai_configs, num)
print('finding the best start state')
best_start = self.playoff_helper(self.start_configs, num)
return best_ai, best_start
class LSStartStateTrainer:
def __init__(self, start=None, n_games=10, start_temp=1024, factor=2):
self.start = start
if start == None:
self.start = get_random_start()
self.delta = 0.00001
self.n_games = n_games
#make both of them random, but the simpel start state
self.player_config = {
Settings.AI.value: True, #should this be Ai or person
Settings.START_TYPE.value:
StartType.SIMPLE.value, #what kind of start state
Settings.START_PARAMS.value: [], #any parameters for the stater
Settings.SEARCH_TYPE.value:
SearchType.RANDOM.value, #what kind of search is happening
Settings.SEARCH_PARAMS.value: [], #any parameters for the search
Settings.AI_TYPE.value: AIType.NONE.value, # what is the AI
Settings.AI_PARAMS.value: [] #any params for the ai
}
self.opponent_config = {
Settings.AI.value: True, #should this be Ai or person
Settings.START_TYPE.value:
StartType.CHAMPION.value, #what kind of start state
Settings.START_PARAMS.value: [], #any parameters for the stater
Settings.SEARCH_TYPE.value:
SearchType.RANDOM.value, #what kind of search is happening
Settings.SEARCH_PARAMS.value: [], #any parameters for the search
Settings.AI_TYPE.value: AIType.NONE.value, # what is the AI
Settings.AI_PARAMS.value: [] #any params for the ai
}
self.engine = Engine(1000)
self.best, stats = self.simulated_annealing(start_temp, factor)
print(self.best)
f = open("train/simulated_annealing_results.txt", "a")
t = open("train/simulated_annealing_factors.txt", "a")
s = open("train/simuated_annealing_start_state.txt", "a")
g = open("train/simulated_annealing_stats.txt", "a")
f.write("{}\n".format(self.best))
t.write("{}\n".format([start_temp, factor]))
s.write("{}\n".format(self.start))
g.write("{}\n".format(stats))
g.close()
s.close()
t.close()
f.close()
def evaluate(self, last_start, next_start):
wins = 0
self.player_config[Settings.START_PARAMS.value] = next_start
self.opponent_config[Settings.START_PARAMS.value] = last_start
for i in range(self.n_games):
self.engine.restart(1000, self.player_config, self.opponent_config)
res = self.engine.run()
if res == 0:
wins += 0.5
else:
wins += (res == 1)
print(float(self.n_games - wins) / self.n_games)
return float(self.n_games - wins) / self.n_games
def get_random_next(self, last_start):
i = int(random.random() * len(last_start))
j = i
while j == i:
j = random.random() * len(last_start)
next_start = copy.copy(last_start)
temp = next_start[i]
next_start[i] = next_start[j]
next_start[j] = temp
return next_start
def get_similar_random_next(self, last_start):
#want to swap two neighboring pieces
s1 = int(random.random() * len(last_start))
i1, j1 = s1 / 10, s1 % 10
s2 = s1
valid = False
while (not valid):
n = int(random.random() * 8)
count = 0
for i2 in range(i1 - 1, i1 + 2):
if valid: break
for j2 in range(j1 - 1, j1 + 2):
if valid: break
if (i1 == i2 and j1 == j2): continue
s2 = 10 * i2 + j2
if (count == n):
if (not s2 < 0) and (not s2 >= len(last_start)):
#print(s2)
valid = True
count += 1
next_start = copy.copy(last_start)
#print("s1 : {}".format(s1))
#print("s2 : {}".format(s2))
temp = next_start[s1]
next_start[s1] = next_start[s2]
next_start[s2] = temp
return next_start
def simulated_annealing(self, start_temp, factor):
cur = self.start
temp = start_temp
stats = []
while True:
if temp <= self.delta: return cur, stats
next_start = self.get_similar_random_next(cur)
e = self.evaluate(cur, next_start)
stats.append(e)
if (e > 0.5):
cur = next_start
else:
should_use_anyways = random.random()
p = np.exp((e - 0.5) / temp)
if (should_use_anyways < p):
cur = next_start
temp = temp / factor
