def make_env_stack(num_envs,
game_path,
base_port,
game_log_path,
opp_fp_and_elo,
trainee_elo,
elo_match=True,
survivor=False,
stdout_path=None,
level_path=None,
image_based=False,
time_reward=0.,
env_p=3):
if num_envs >= 1:
envs = []
for i in range(num_envs):
envs.append(lambda game_path=game_path, b=base_port +
(i * 2), c=game_log_path.replace(
".txt", "-" + str(i) + ".txt"), d=opp_fp_and_elo, e
=elo_match, f=trainee_elo, g=survivor, h=stdout_path.
replace(".txt", "-" + str(i) + ".txt"), i=level_path, j
=image_based, k=time_reward: TankEnv(game_path,
game_port=b,
game_log_path=c,
opp_fp_and_elo=d,
elo_match=e,
center_elo=f,
survivor=g,
stdout_path=h,
verbose=True,
level_path=i,
image_based=j,
time_reward=k,
p=env_p))
if num_envs == 1:
env_stack = SubprocVecEnv(envs, start_method="fork")
else:
env_stack = SubprocVecEnv(envs, start_method="forkserver")
env_stack.reset()
return env_stack
else:
env = TankEnv(game_path,
game_port=base_port,
game_log_path=game_log_path,
opp_fp_and_elo=opp_fp_and_elo,
elo_match=elo_match,
center_elo=trainee_elo,
survivor=survivor,
stdout_path=stdout_path,
level_path=level_path,
image_based=image_based,
time_reward=time_reward,
p=env_p)
env.reset()
return env
def run_model(args):
env = TankEnv(args.game_path,
opp_fp_and_elo=[(args.opp, 1000)],
game_port=args.base_port,
my_port=args.my_port,
image_based=args.image_based,
level_path=args.level_path,
rand_opp=args.rand_opp,
p=args.env_p,
opp_p=args.opp_env_p)
model = None
if args.p1:
model = PPO.load(args.p1)
elif args.p1same:
model = PPO.load(args.opp)
score = [0, 0, 0]
print("Score: [Player1 Wins, Player2 Wins, Ties]")
obs = env.reset()
if args.image_based and (args.ai_view or args.rev_ai_view):
fig = plt.gcf()
fig.show()
fig.canvas.draw()
while True:
if args.image_based and (args.ai_view or args.rev_ai_view):
if not args.rev_ai_view:
plt.imshow(obs, origin="lower")
else:
plt.imshow(env.opp_state, origin="lower")
fig.canvas.draw()
if model:
action, _ = model.predict(obs)
elif args.rand_p1:
action = np.random.rand(5) * 2 - 1
else:
action = np.zeros(5, dtype=np.float32)
obs, reward, done, info = env.step(action)
if done:
score[info["winner"]] += 1
print("Score:", score)
obs = env.reset()
class AIMatchmaker(gym.Env):
metadata = {'render.modes': None}
def __init__(self,
all_stats,
all_opps,
all_elos,
game_path,
model_dir,
base_port=50000,
my_port=50001,
image_based=False,
level_path=None,
env_p=3,
starting_elo=None,
K=16,
D=5.,
time_reward=-0.003,
matchmaking_mode=0,
elo_log_interval=10000,
win_loss_ratio=[0, 0]):
super(AIMatchmaker, self).__init__()
self.all_stats = combine_winrates(all_stats)
self.all_opps = all_opps
self.all_elos = all_elos
self.model_dir = model_dir
self.agent_elo = starting_elo if starting_elo != None else self.all_elos[
0]
self.env = TankEnv(game_path,
opp_fp_and_elo=[],
game_port=base_port,
my_port=my_port,
image_based=image_based,
level_path=level_path,
p=env_p,
time_reward=time_reward)
self.action_space = self.env.action_space
self.observation_space = self.env.observation_space
self.K = K
self.D = D
self.my_port = my_port
self.mm = matchmaking_mode
self.uncounted_games = np.array([0, 0], dtype=np.uint32)
self.counted_game_sets = 0
self.win_loss_ratio = np.array(win_loss_ratio, dtype=np.uint32)
self.started = False
self.next_opp()
self.elo_log_interval = elo_log_interval
self.num_steps = 0
self.elo_log = []
def next_opp(self):
weights = np.zeros((len(self.all_elos)), dtype=np.float32)
if self.mm == 1:
# ELO based matchmaking, where ELOs closer to agent ELo is prefered (but not guarenteed)
weights += np.array([
weight_func(elo - self.agent_elo, self.D)
for elo in self.all_elos
],
dtype=np.float32)
if any(self.win_loss_ratio):
while all(self.uncounted_games >= self.win_loss_ratio):
self.uncounted_games -= self.win_loss_ratio
self.counted_game_sets += 1
tmp = self.uncounted_games >= self.win_loss_ratio
if tmp[0] and not tmp[1]:
# Need more losses
if self.mm == 1:
# Zero weights for opponents that have <= ELOs than agent
for i, elo in enumerate(self.all_elos):
if elo <= self.agent_elo:
weights[i] = 0
# Choose agent with highest ELO if agent ELO is higher than all opponent ELOs
if sum(weights) == 0:
weights[self.all_elos.index(max(self.all_elos))] = 1
else:
# Equal probability for opponents that have > ELOs than agent
for i, elo in enumerate(self.all_elos):
if elo > self.agent_elo:
weights[i] = 1
# Choose agent with highest ELO if agent ELO is higher than all opponent ELOs
if sum(weights) == 0:
weights[self.all_elos.index(max(self.all_elos))] = 1
elif not tmp[0] and tmp[1]:
# Need more wins
if self.mm == 1:
# Zero weights for opponents that have >= ELOs than agent
for i, elo in enumerate(self.all_elos):
if elo >= self.agent_elo:
weights[i] = 0
# Choose agent with lowest ELO if agent ELO is higher than all opponent ELOs
if sum(weights) == 0:
weights[self.all_elos.index(min(self.all_elos))] = 1
else:
# Equal probability for opponents that have < ELOs than agent
for i, elo in enumerate(self.all_elos):
if elo < self.agent_elo:
weights[i] = 1
# Choose agent with highest ELO if agent ELO is higher than all opponent ELOs
if sum(weights) == 0:
weights[self.all_elos.index(min(self.all_elos))] = 1
self.current_opp_idx = choice_with_normalization(
[i for i in range(len(self.all_elos))], weights)
self.current_opp = self.all_opps[self.current_opp_idx]
self.current_opp_elo = self.all_elos[self.current_opp_idx]
#print("thread", self.my_port, "current opp elo:", self.current_opp_elo, "agent elo:", self.agent_elo, flush=True)
self.env.load_new_opp(0, opp_fp(self.model_dir, self.current_opp),
self.current_opp_elo)
def get_agent_elo(self):
return self.agent_elo
def reset(self):
if self.started:
last_winner = self.env.last_winner
if last_winner == 0:
win_rate = 1.
self.uncounted_games[0] += 1
elif last_winner == 1:
win_rate = 0.
self.uncounted_games[1] += 1
else:
win_rate = .5
agent_elo_change, _ = elo_change(self.agent_elo,
self.current_opp_elo, self.K,
win_rate)
self.agent_elo += int(agent_elo_change)
#print("THREAD", self.my_port, "CURRENT AGENT ELO:", self.agent_elo, flush=True)
else:
self.started = True
self.next_opp()
return self.env.reset()
def step(self, action):
if self.num_steps % self.elo_log_interval == 0:
self.elo_log.append(self.agent_elo)
self.num_steps += 1
return self.env.step(action)
def render(self, mode='console'):
raise NotImplementedError()
def close(self):
self.env.close()
image_based=args.image_based,
level_path=args.level_path,
rand_opp=True,
p=args.env_p)
if args.image_based:
model = PPO("CnnPolicy", env, n_steps=64)
else:
model = PPO("MlpPolicy", env, n_steps=64)
print(model.policy)
try:
if args.train:
model.learn(total_timesteps=args.num_steps)
else:
obs = env.reset()
if args.image_based and args.ai_view:
fig = plt.gcf()
fig.show()
fig.canvas.draw()
for _ in tqdm(range(args.num_steps)):
if args.image_based and args.ai_view:
plt.imshow(obs, origin="lower", interpolation='none')
fig.canvas.draw()
if model:
action, _ = model.predict(obs)
elif args.rand_p1:
action = np.random.rand(5) * 2 - 1
else:
action = np.zeros(5, dtype=np.float32)
obs, reward, done, info = env.step(action)