applied formatter to envs
This commit is contained in:
NM512
177
envs/dmlab.py
177
envs/dmlab.py
@@ -4,98 +4,105 @@ import deepmind_lab
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class DeepMindLabyrinth(object):
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ACTION_SET_DEFAULT = (
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(0, 0, 0, 1, 0, 0, 0), # Forward
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(0, 0, 0, -1, 0, 0, 0), # Backward
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(0, 0, -1, 0, 0, 0, 0), # Strafe Left
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(0, 0, 1, 0, 0, 0, 0), # Strafe Right
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(-20, 0, 0, 0, 0, 0, 0), # Look Left
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(20, 0, 0, 0, 0, 0, 0), # Look Right
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(-20, 0, 0, 1, 0, 0, 0), # Look Left + Forward
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(20, 0, 0, 1, 0, 0, 0), # Look Right + Forward
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(0, 0, 0, 0, 1, 0, 0), # Fire
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)
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ACTION_SET_DEFAULT = (
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(0, 0, 0, 1, 0, 0, 0), # Forward
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(0, 0, 0, -1, 0, 0, 0), # Backward
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(0, 0, -1, 0, 0, 0, 0), # Strafe Left
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(0, 0, 1, 0, 0, 0, 0), # Strafe Right
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(-20, 0, 0, 0, 0, 0, 0), # Look Left
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(20, 0, 0, 0, 0, 0, 0), # Look Right
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(-20, 0, 0, 1, 0, 0, 0), # Look Left + Forward
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(20, 0, 0, 1, 0, 0, 0), # Look Right + Forward
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(0, 0, 0, 0, 1, 0, 0), # Fire
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)
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ACTION_SET_MEDIUM = (
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(0, 0, 0, 1, 0, 0, 0), # Forward
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(0, 0, 0, -1, 0, 0, 0), # Backward
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(0, 0, -1, 0, 0, 0, 0), # Strafe Left
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(0, 0, 1, 0, 0, 0, 0), # Strafe Right
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(-20, 0, 0, 0, 0, 0, 0), # Look Left
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(20, 0, 0, 0, 0, 0, 0), # Look Right
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(0, 0, 0, 0, 0, 0, 0), # Idle.
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)
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ACTION_SET_MEDIUM = (
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(0, 0, 0, 1, 0, 0, 0), # Forward
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(0, 0, 0, -1, 0, 0, 0), # Backward
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(0, 0, -1, 0, 0, 0, 0), # Strafe Left
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(0, 0, 1, 0, 0, 0, 0), # Strafe Right
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(-20, 0, 0, 0, 0, 0, 0), # Look Left
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(20, 0, 0, 0, 0, 0, 0), # Look Right
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(0, 0, 0, 0, 0, 0, 0), # Idle.
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)
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ACTION_SET_SMALL = (
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(0, 0, 0, 1, 0, 0, 0), # Forward
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(-20, 0, 0, 0, 0, 0, 0), # Look Left
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(20, 0, 0, 0, 0, 0, 0), # Look Right
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)
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ACTION_SET_SMALL = (
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(0, 0, 0, 1, 0, 0, 0), # Forward
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(-20, 0, 0, 0, 0, 0, 0), # Look Left
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(20, 0, 0, 0, 0, 0, 0), # Look Right
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)
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def __init__(
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self,
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level,
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mode,
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action_repeat=4,
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render_size=(64, 64),
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action_set=ACTION_SET_DEFAULT,
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level_cache=None,
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seed=None,
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runfiles_path=None,
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):
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assert mode in ("train", "test")
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if runfiles_path:
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print("Setting DMLab runfiles path:", runfiles_path)
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deepmind_lab.set_runfiles_path(runfiles_path)
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self._config = {}
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self._config["width"] = render_size[0]
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self._config["height"] = render_size[1]
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self._config["logLevel"] = "WARN"
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if mode == "test":
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self._config["allowHoldOutLevels"] = "true"
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self._config["mixerSeed"] = 0x600D5EED
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self._action_repeat = action_repeat
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self._random = np.random.RandomState(seed)
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self._env = deepmind_lab.Lab(
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level="contributed/dmlab30/" + level,
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observations=["RGB_INTERLEAVED"],
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config={k: str(v) for k, v in self._config.items()},
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level_cache=level_cache,
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)
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self._action_set = action_set
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self._last_image = None
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self._done = True
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def __init__(
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self, level, mode, action_repeat=4, render_size=(64, 64),
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action_set=ACTION_SET_DEFAULT, level_cache=None, seed=None,
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runfiles_path=None):
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assert mode in ('train', 'test')
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if runfiles_path:
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print('Setting DMLab runfiles path:', runfiles_path)
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deepmind_lab.set_runfiles_path(runfiles_path)
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self._config = {}
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self._config['width'] = render_size[0]
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self._config['height'] = render_size[1]
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self._config['logLevel'] = 'WARN'
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if mode == 'test':
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self._config['allowHoldOutLevels'] = 'true'
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self._config['mixerSeed'] = 0x600D5EED
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self._action_repeat = action_repeat
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self._random = np.random.RandomState(seed)
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self._env = deepmind_lab.Lab(
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level='contributed/dmlab30/'+level,
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observations=['RGB_INTERLEAVED'],
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config={k: str(v) for k, v in self._config.items()},
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level_cache=level_cache)
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self._action_set = action_set
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self._last_image = None
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self._done = True
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@property
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def observation_space(self):
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shape = (self._config["height"], self._config["width"], 3)
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space = gym.spaces.Box(low=0, high=255, shape=shape, dtype=np.uint8)
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return gym.spaces.Dict({"image": space})
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@property
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def observation_space(self):
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shape = (self._config['height'], self._config['width'], 3)
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space = gym.spaces.Box(low=0, high=255, shape=shape, dtype=np.uint8)
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return gym.spaces.Dict({'image': space})
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@property
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def action_space(self):
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return gym.spaces.Discrete(len(self._action_set))
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@property
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def action_space(self):
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return gym.spaces.Discrete(len(self._action_set))
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def reset(self):
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self._done = False
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self._env.reset(seed=self._random.randint(0, 2**31 - 1))
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obs = self._get_obs()
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return obs
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def reset(self):
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self._done = False
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self._env.reset(seed=self._random.randint(0, 2 ** 31 - 1))
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obs = self._get_obs()
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return obs
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def step(self, action):
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raw_action = np.array(self._action_set[action], np.intc)
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reward = self._env.step(raw_action, num_steps=self._action_repeat)
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self._done = not self._env.is_running()
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obs = self._get_obs()
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return obs, reward, self._done, {}
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def step(self, action):
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raw_action = np.array(self._action_set[action], np.intc)
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reward = self._env.step(raw_action, num_steps=self._action_repeat)
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self._done = not self._env.is_running()
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obs = self._get_obs()
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return obs, reward, self._done, {}
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def render(self, *args, **kwargs):
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if kwargs.get("mode", "rgb_array") != "rgb_array":
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raise ValueError("Only render mode 'rgb_array' is supported.")
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del args # Unused
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del kwargs # Unused
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return self._last_image
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def render(self, *args, **kwargs):
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if kwargs.get('mode', 'rgb_array') != 'rgb_array':
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raise ValueError("Only render mode 'rgb_array' is supported.")
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del args # Unused
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del kwargs # Unused
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return self._last_image
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def close(self):
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self._env.close()
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def close(self):
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self._env.close()
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def _get_obs(self):
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if self._done:
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image = 0 * self._last_image
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else:
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image = self._env.observations()['RGB_INTERLEAVED']
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self._last_image = image
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return {'image': image}
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def _get_obs(self):
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if self._done:
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image = 0 * self._last_image
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else:
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image = self._env.observations()["RGB_INTERLEAVED"]
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self._last_image = image
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return {"image": image}
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