class StreetFighterEnv(gym.Env):
def __init__(self, index, difficulty, monitor=None):
roms_path = "roms/"
self.env = Environment("env{}".format(index),
roms_path,
difficulty=difficulty)
if monitor:
self.monitor = monitor
else:
self.monitor = None
self.env.start()
self.action_space = gym.spaces.Discrete(90)
self.observation_space = gym.spaces.Box(low=0,
high=1,
shape=(3 + self.action_space.n,
128, 128),
dtype=np.float32)
def step(self, action):
move_action = action // 10
attack_action = action % 10
frames, reward, round_done, stage_done, game_done = self.env.step(
move_action, attack_action)
if self.monitor:
for frame in frames:
self.monitor.record(frame)
states = np.zeros(self.observation_space.shape, dtype=np.float32)
if not (round_done or stage_done or game_done):
states[:3, :] = process_frame(frames[-1])
else:
self.env.reset()
action = 80
states[action + 3, :] = 1
reward = reward["P1"] / 10
if stage_done:
reward += 3
elif game_done:
reward -= 5
info = {
'stage_done': stage_done,
'round_done': round_done,
'stage': self.env.stage
}
return states, reward, game_done, info
def reset(self):
self.env.new_game()
states = np.zeros(self.observation_space.shape, dtype=np.float32)
states[80 + 3, :] = 1
return states
def __exit__(self, *args):
return self.env.close()
def main(argv):
roms_path = "roms/"
env = Environment("env1", roms_path)
policy_gradient = PolicyGradientBuilder(100800, 100800, False)
rl = AgentProxy(env, 100800)
dqn = DQNFlyweight(agent=rl)
net = ReinforcementLearning(rl)
env.start()
while True:
move_action = random.randint(0, 8)
p_move_action = rl.action_space_down_sample(move_action)
steps_move_action = net.steps_action(p_move_action)
attack_action = random.randint(0, 9)
p_attack_action = rl.action_space_down_sample(attack_action)
steps_attack_action = net.steps_action(p_attack_action)
#frames, reward, round_done, stage_done, game_done = env.step(move_action, attack_action)
frames, reward, \
round_done, stage_done, \
game_done = policy_gradient.learn(steps_move_action, steps_attack_action)
if game_done:
env.new_game()
elif stage_done:
env.next_stage()
elif round_done:
env.next_round()
def run_env(worker_id, roms_path):
env = Environment(f"env{worker_id}", roms_path)
env.start()
while True:
move_action = random.randint(0, 8)
attack_action = random.randint(0, 9)
frames, reward, round_done, stage_done, game_done = env.step(
move_action, attack_action)
if game_done:
env.new_game()
elif stage_done:
env.next_stage()
elif round_done:
env.next_round()
class StreetFighterEnv(object):
def __init__(self, index, monitor=None):
roms_path = "/home/zhangchao/Downloads/"
self.env = Environment("env{}".format(index), roms_path)
if monitor:
self.monitor = monitor
else:
self.monitor = None
self.env.start()
# 单步执行返回数据：游戏画面，一轮结束，场景切换，游戏结束
def step(self, action):
move_action = action // 10
attack_action = action % 10
frames, reward, round_done, stage_done, game_done = self.env.step(
move_action, attack_action)
if self.monitor:
for frame in frames:
# 通过管道pipe为子进程写入图像数据
self.monitor.record(frame)
if not (round_done or stage_done or game_done):
# frame拼接
frames = np.concatenate([process_frame(frame) for frame in frames],
0)[None, :, :, :].astype(np.float32)
else:
frames = np.zeros((1, 3, 168, 168), dtype=np.float32)
# 奖励设计
reward = reward["P1"]
if stage_done:
reward = 25
elif game_done:
reward = -50
reward *= (1 + (self.env.stage - 1) / 10)
reward /= 10
return frames, reward, round_done, stage_done, game_done
# 重启
def reset(self, round_done, stage_done, game_done):
if game_done:
self.env.new_game()
elif stage_done:
self.env.next_stage()
elif round_done:
self.env.next_round()
return np.zeros((1, 3, 168, 168), dtype=np.float32)
def run(worker_no, roms_path, learning_rate, cluster, data_bins, stats,
saves_path, save_frequency):
name = "worker%d" % worker_no
with tf.device(
tf.train.replica_device_setter(
worker_device="/job:worker/task:%d" % worker_no,
cluster=cluster)):
Model("global", learning_rate)
saver = tf.train.Saver(var_list=tf.get_collection(
tf.GraphKeys.TRAINABLE_VARIABLES, scope="global"))
local_model = Model(name, learning_rate)
update_local_ops = update_target_graph('global', name)
gpu_options = tf.GPUOptions(allow_growth=True)
config = tf.ConfigProto(gpu_options=gpu_options)
server = tf.train.Server(cluster,
job_name="worker",
task_index=worker_no,
config=config)
with tf.train.MonitoredTrainingSession(master=server.target) as sess:
try:
if stats.get_episode() != 0:
saver.restore(sess._sess._sess._sess._sess,
f'{saves_path}/model.ckpt')
sess.run(update_local_ops)
print("Started Worker Updates")
env = Environment(name, roms_path, difficulty=3)
frames = env.start()
while True:
history = {
"observation": [],
"move_action": [],
"attack_action": [],
"reward": []
}
game_done = False
total_reward = 0
while not game_done:
observation = wu.prepro(frames)
history["observation"].append(observation)
move_out, attack_out = sess.run(
[local_model.move_out_sym, local_model.attack_out_sym],
feed_dict={local_model.observation_sym: observation})
move_action_hot = wu.choose_action(move_out)
attack_action_hot = wu.choose_action(attack_out)
history["move_action"].append(move_action_hot)
history["attack_action"].append(attack_action_hot)
frames, r, round_done, stage_done, game_done = env.step(
np.argmax(move_action_hot),
np.argmax(attack_action_hot))
total_reward += r["P1"]
history["reward"].append(r["P1"])
if round_done:
wu.store_history(data_bins, worker_no, history)
history = {
"observation": [],
"move_action": [],
"attack_action": [],
"reward": []
}
if game_done:
wu.train(sess, local_model,
*data_bins.empty_bin(worker_no))
sess.run(update_local_ops)
stats.update({
"score": total_reward,
"stage": env.stage
})
if stats.get_episode() > 0 and stats.get_episode(
) % save_frequency == 0:
saver.save(sess._sess._sess._sess._sess,
f'{saves_path}/model.ckpt')
frames = env.reset()
except:
error = traceback.format_exc()
print(error)
logging.error(error)
exit(1)
import random
import time
from MAMEToolkit.sf_environment import Environment
roms_path = 'roms/' # Replace this with the path to your ROMs
env = Environment('env1', roms_path)
print('[test] Loaded SF3 from ROM file')
# https://github.com/M-J-Murray/MAMEToolkit/blob/3041734391292376aa909938ea5b51030e3c0240/MAMEToolkit/sf_environment/Environment.py#L88
print('[test] Wait until learnable gameplay starts...')
frames = env.start()
print('[test] Start!')
env.close()
print('[test] Your installation is complete!')
class Worker(mp.Process):
def __init__(self, env_id, roms_path, difficulty, epoch_size, model, optim,
criterion, rewardQueue, frameRatio, framesPerStep):
super(Worker, self).__init__()
self.env_id = env_id
self.roms_path = roms_path
self.difficulty = difficulty
self.epoch_size = epoch_size
self.model = model
self.optim = optim
self.criterion = criterion
self.rewardQueue = rewardQueue
self.frameRatio = frameRatio
self.framesPerStep = framesPerStep
def run(self):
try:
logger.info("Starting Worker")
self.env = Environment(self.env_id,
self.roms_path,
difficulty=self.difficulty,
frame_ratio=self.frameRatio,
frames_per_step=self.framesPerStep,
throttle=False)
frames = self.env.start()
while True:
self.model.eval()
observations, histories, frames = self.generate_playthrough(
frames)
self.model.train()
dataset = wu.compileHistories(observations, histories)
wu.train(self.model, self.optim, self.criterion, dataset)
except Exception as identifier:
logger.error(identifier)
logger.error(traceback.format_exc())
def generate_playthrough(self, frames):
observations = [[]]
histories = [{"moveAction": [], "attackAction": [], "reward": []}]
epoch_reward = 0
total_round = 0
game_done = False
for i in range(self.epoch_size):
while not game_done:
x = wu.prepro(frames)
observations[total_round].append(x.cpu())
moveOut, attackOut = self.model(Variable(x))
moveAction = wu.chooseAction(F.softmax(moveOut, dim=1))
attackAction = wu.chooseAction(F.softmax(attackOut, dim=1))
histories[total_round]["moveAction"].append(
torch.FloatTensor(1).fill_(moveAction))
histories[total_round]["attackAction"].append(
torch.FloatTensor(1).fill_(attackAction))
frames, r, round_done, stage_done, game_done = self.env.step(
moveAction, attackAction)
histories[total_round]["reward"].append(
torch.FloatTensor(1).fill_(r["P1"]))
epoch_reward += r["P1"]
if round_done:
total_round += 1
histories.append({
"moveAction": [],
"attackAction": [],
"reward": []
})
observations.append([])
if game_done:
self.rewardQueue.put({
"reward": epoch_reward,
"stage": self.env.stage
})
frames = self.env.reset()
return observations, histories, frames
import random
from MAMEToolkit.sf_environment import Environment
roms_path = 'rom/'
env = Environment("sfiii3n", roms_path)
env.start()
while True:
move_action = random.randint(0, 8)
attack_action = random.randint(0, 9)
frames, reward, round_done, stage_done, game_done = env.step(
move_action, attack_action)
if game_done:
env.new_game()
elif stage_done:
env.next_stage()
elif round_done:
env.next_round()
class MacroStreetFighterEnv(gym.Env):
def __init__(self, index, difficulty, monitor=None):
roms_path = "roms/"
self.env = Environment("env{}".format(index),
roms_path,
difficulty=difficulty)
if monitor:
self.monitor = monitor
else:
self.monitor = None
self.env.start()
self.action_space = gym.spaces.Discrete(18 + MACRO_NUMS)
self.observation_space = gym.spaces.Box(low=0,
high=1,
shape=(3 + self.action_space.n,
128, 128),
dtype=np.float32)
def step(self, action):
frames, reward, round_done, stage_done, game_done = self.step_(action)
if self.monitor:
for frame in frames:
self.monitor.record(frame)
states = np.zeros(self.observation_space.shape, dtype=np.float32)
if not (round_done or stage_done or game_done):
states[:3, :] = process_frame(frames[-1])
else:
self.env.reset()
action = 8
states[action + 3, :] = 1
reward = reward["P1"] / 10
if stage_done:
reward += 3
elif game_done:
reward -= 5
info = {
'stage_done': stage_done,
'round_done': round_done,
'stage': self.env.stage
}
return states, reward, game_done, info
def step_(self, action):
if self.env.started:
if not self.env.round_done and not self.env.stage_done and not self.env.game_done:
if action < 9:
actions = index_to_move_action(action)
elif action < 18:
actions = index_to_attack_action(action - 9)
elif action < 18 + MACRO_NUMS:
actions = index_to_comb[action - 18]()
else:
raise EnvironmentError("Action out of range")
if action < 18:
data = self.env.gather_frames(actions)
else:
data = self.sub_step_(actions)
data = self.env.check_done(data)
return data["frame"], data[
"rewards"], self.env.round_done, self.env.stage_done, self.env.game_done
else:
raise EnvironmentError(
"Attempted to step while characters are not fighting")
else:
raise EnvironmentError("Start must be called before stepping")
def sub_step_(self, actions):
frames = []
for step in actions:
for i in range(step["hold"]):
data = self.env.emu.step(
[action.value for action in step["actions"]])
frames.append(data['frame'])
data = self.env.emu.step([])
frames.append(data['frame'])
p1_diff = (self.env.expected_health["P1"] - data["healthP1"])
p2_diff = (self.env.expected_health["P2"] - data["healthP2"])
self.env.expected_health = {
"P1": data["healthP1"],
"P2": data["healthP2"]
}
rewards = {"P1": (p2_diff - p1_diff), "P2": (p1_diff - p2_diff)}
data["rewards"] = rewards
data["frame"] = frames
return data
def reset(self):
self.env.new_game()
states = np.zeros(self.observation_space.shape, dtype=np.float32)
states[8 + 3, :] = 1
return states
def __exit__(self, *args):
return self.env.close()
2] # greyscale
return frame
isGrey = True
roms_path = "../roms/" # Replace this with the path to your ROMs
env = Environment("env1",
roms_path,
frames_per_step=1,
frame_ratio=3,
throttle=False)
fig = plt.figure()
plt.ion()
im: AxesImage = plt.imshow(prepro(env.start(), isGrey),
cmap="gray" if isGrey else None)
plt.axis("off")
plt.show()
while True:
move_action = random.randint(0, 8)
attack_action = random.randint(0, 9)
frame, reward, round_done, stage_done, game_done = env.step(
move_action, attack_action)
im.set_data(prepro(frame, isGrey))
plt.pause(0.0001)
if game_done:
env.new_game()
elif stage_done:
env.next_stage()
elif round_done:
