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)
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()
