def _thunk():
if "mario" in env_id:
env = gym_super_mario_bros.make('SuperMarioBros-v0')
env = JoypadSpace(env, SIMPLE_MOVEMENT)
elif env_id.startswith("dm"):
_, domain, task = env_id.split('.')
env = dm_control2gym.make(domain_name=domain, task_name=task)
else:
env = gym.make(env_id)
is_atari = hasattr(gym.envs, 'atari') and isinstance(
env.unwrapped, gym.envs.atari.atari_env.AtariEnv)
if is_atari:
env = make_atari(env_id)
env.seed(seed + rank)
if log_dir is not None:
env = bench.Monitor(env, os.path.join(log_dir, str(rank)))
if is_atari:
env = wrap_deepmind(env, clip_rewards=False)
# If the input has shape (W,H,3), wrap for PyTorch convolutions
obs_shape = env.observation_space.shape
if "mario" in env_id:
env = wrap_deepmind(env, clip_rewards=False, episode_life=False)
env = WrapPyTorchMario(env)
elif len(obs_shape) == 3 and obs_shape[2] in [1, 3]:
env = WrapPyTorch(env)
return env
def from_args(cls, args, seed, **kwargs):
env = gym_super_mario_bros.make(args.env)
env = JoypadSpace(env, [["right"], ["right", "A"]])
env = SkipFrame(env, skip=args.skip_rate)
env._max_episode_steps = args.max_episode_length
env.seed(seed)
return cls(
env, args.frame_stack, args.skip_rate, args.max_episode_length
)
def fn():
env = gym.make(env_id)
if 'SuperMarioBros' in env_id:
env = JoypadSpace(env, actions)
env = ReshapeReward(env, monitor=None)
env = SkipObs(env)
env = SingleEnv(env)
env.seed(seed + rank)
env.action_space.seed(seed + rank)
return env
def create(self, env_id, seed=None):
try:
env = gym.make(env_id)
env = JoypadSpace(env, SIMPLE_MOVEMENT)
if seed:
env.seed(seed)
except gym.error.Error:
raise InvalidUsage("Attempted to look up malformed environment ID '{}'".format(env_id))
instance_id = str(uuid.uuid4().hex)[:self.id_len]
self.envs[instance_id] = env
return instance_id
def wrap_():
env = gym_super_mario_bros.make(env_name)
env = JoypadSpace(env, [["right"], ["right", "A"]])
env.seed(seed)
env = Monitor(env, './')
env = MaxAndSkip(env, skip=4)
env = ProcessFrame84(env)
env = ImageToPytorch(env)
env = FrameStack(env, 4)
env = ClipReward(env)
return env
def make_env():
env = gym_super_mario_bros.make('SuperMarioBros-v3')
env = JoypadSpace(env, RIGHT_ONLY)
env = CustomRewardAndDoneEnv(env) # 報酬とエピソード完了の変更
env = StochasticFrameSkip(env, n=4, stickprob=0.25) # スティッキーフレームスキップ
env = Downsample(env, 2) # ダウンサンプリング
env = FrameStack(env, 4) # フレームスタック
env = ScaledFloatFrame(env) # 状態の正規化
env = Monitor(env, log_dir, allow_early_resets=True)
env.seed(0) # シードの指定
set_global_seeds(0)
env = DummyVecEnv([lambda: env]) # ベクトル環境の生成
print('行動空間: ', env.action_space)
print('状態空間: ', env.observation_space)
return env
def make_env(cfg):
"""Helper function to create dm_control environment"""
if cfg.env == 'ball_in_cup_catch':
domain_name = 'ball_in_cup'
task_name = 'catch'
elif cfg.env == 'point_mass_easy':
domain_name = 'point_mass'
task_name = 'easy'
else:
domain_name = cfg.env.split('_')[0]
task_name = '_'.join(cfg.env.split('_')[1:])
# per dreamer: https://github.com/danijar/dreamer/blob/02f0210f5991c7710826ca7881f19c64a012290c/wrappers.py#L26
camera_id = 2 if domain_name == 'quadruped' else 0
# env = dmc2gym.make(domain_name=domain_name,
# task_name=task_name,
# seed=cfg.seed,
# visualize_reward=False,
# from_pixels=True,
# height=cfg.image_size,
# width=cfg.image_size,
# frame_skip=cfg.action_repeat,
# camera_id=camera_id)
# env = gym.make("CarRacing-v0")
env_ = gym_tetris.make('TetrisA-v0')
env = JoypadSpace(env_, SIMPLE_MOVEMENT)
# env = MaxAndSkipEnv(env)
# env._max_episode_steps = env_._max_episode_steps
max_episode_steps = 10000
env = WrapPyTorch(env, max_episode_steps)
env.seed(cfg.seed)
# print(env.ram)
obs = env.reset()
print(obs.shape)
# env.seed(cfg.seed)
env = utils.FrameStack(env, k=cfg.frame_stack)
print("Init env done")
# assert env.action_space.low.min() >= -1
# assert env.action_space.high.max() <= 1
return env
def _thunk():
mario_env = JoypadSpace(SuperMario_Env(world, stage, version),
movement)
if wrap_atari:
mario_env._max_episode_steps = max_episode_steps * 4
mario_env = StickyActionEnv(mario_env)
mario_env = MaxAndSkipEnv(mario_env, skip=4)
mario_env = DummyMontezumaInfoWrapper(mario_env)
mario_env = AddRandomStateToInfo(mario_env)
# mario_env.seed(seed + rank)
mario_env = Monitor(
mario_env,
logger.get_dir()
and os.path.join(logger.get_dir(), str(rank)),
allow_early_resets=True)
if wrap_atari:
mario_env = wrap_deepmind(mario_env)
mario_env = BlocksWrapper(mario_env)
mario_env.seed(seed)
return mario_env
from nes_py.wrappers import JoypadSpace
from Contra.actions import SIMPLE_MOVEMENT, COMPLEX_MOVEMENT, RIGHT_ONLY
ENV_NAME = 'Contra-v0'
CUSTOM_MOVEMENT = [
['right'],
['right', 'A'],
['right', 'B'],
['right', 'A', 'B'],
]
env = gym.make(ENV_NAME)
env = JoypadSpace(env, RIGHT_ONLY)
np.random.seed(120)
env.seed(120)
nb_actions = env.action_space.n
print(env.observation_space.shape)
print(env)
model = Sequential()
model.add(Flatten(input_shape=(1, ) + env.observation_space.shape))
model.add(Reshape(env.observation_space.shape))
model.add(
Conv2D(32, (3, 3),
activation='relu',
input_shape=env.observation_space.shape))
model.add(MaxPooling2D((2, 2)))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(Flatten())
# for idx, key in enumerate(keys):
# axarr[idx].plot(episodes, data[key])
# axarr[idx].set_ylabel(key)
# plt.xlabel('episodes')
# plt.tight_layout()
# if output is None:
# plt.show()
# else:
# plt.savefig(output)
ENV_NAME = 'CustomContra-v2'
# Get the environment and extract the number of actions.
env = gym.make(ENV_NAME)
env = JoypadSpace(env, CUSTOM_MOVEMENT)
np.random.seed(123)
env.seed(123)
nb_actions = env.action_space.n
print(nb_actions)
print(env.observation_space.shape)
obs_dim = env.observation_space.shape[0]
# Next, we build a very simple model.
model = Sequential()
model.add(Flatten(input_shape=(1, ) + env.observation_space.shape))
model.add(Dense(16))
model.add(Activation('relu'))
model.add(Dense(16))
model.add(Activation('relu'))
model.add(Dense(16))
model.add(Activation('relu'))
model.add(Dense(nb_actions))
print(device)
Tensor = torch.Tensor
LongTensor = torch.LongTensor
env = gym_super_mario_bros.make('SuperMarioBros-v0')
#env = BinarySpaceToDiscreteSpaceEnv(env, SIMPLE_MOVEMENT)
env = JoypadSpace(env, SIMPLE_MOVEMENT)
#directory = './MarioVideos/'
directory = './MarioVideosLong/'
env = gym.wrappers.Monitor(
env, directory, video_callable=lambda episode_id: episode_id % 5000 == 0)
seed_value = 23
env.seed(seed_value)
torch.manual_seed(seed_value)
random.seed(seed_value)
###### PARAMS ######
learning_rate = 0.0001
#num_episodes = 5000
num_episodes = 9999999999
startNum = 500
#newModel = False
newModel = False
gamma = 0.99
hidden_layer = 512
def run_agent(agent, rendering=False, monitoring=False, print_reward=False):
env = gym_super_mario_bros.make("SuperMarioBros-v0")
env = JoypadSpace(env, SIMPLE_MOVEMENT)
env.seed(42)
if monitoring:
env = Monitor(env, './video', force=True)
agent.eval()
state = env.reset()
if rendering:
env.render()
#Conv2d without flatten()
state = convert_image(state) #.flatten()
state_list = [state, state, state, state]
position = -1
global_reward = 0
s = 0
for _ in range(10000):
#Conv2d input
input = torch.from_numpy(np.array(state_list)).type('torch.FloatTensor')\
.unsqueeze(0)
#Linear input
#input = torch.tensor(state_list).type("torch.FloatTensor").view(1,-1)
output_probabilities = agent(input).detach().numpy()[0]
action = np.random.choice(range(action_count), 1, \
p=output_probabilities).item()
new_state, reward, done, info = env.step(action)
global_reward += reward
s = s + 1
if rendering:
env.render()
state_list.pop()
#Conv2d without flatten()
state_list.append(convert_image(new_state)) #.flatten())
# if mario gets stuck, it gets punished and the loop gets broken
if position == info["x_pos"]:
stuck += 1
if stuck == 100:
global_reward -= 100
break
else:
stuck = 0
position = info["x_pos"]
#env.render()
#Mario died
if info["life"] < 2:
break
if print_reward:
print(global_reward)
return global_reward
