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()