def __init__(self):
self.min_position = -1.2
self.max_position = 0.6
self.max_speed = 0.07
self.goal_position = 0.5
self.low = np.array([self.min_position, -self.max_speed])
self.high = np.array([self.max_position, self.max_speed])
self.viewer = None
self.action_space = spaces.Discrete(3)
self.observation_space = spaces.Box(self.low, self.high)
self._seed()
self.reset()
python类Box()的实例源码
def __init__(self):
self.min_action = -1.0
self.max_action = 1.0
self.min_position = -1.2
self.max_position = 0.6
self.max_speed = 0.07
self.goal_position = 0.45 # was 0.5 in gym, 0.45 in Arnaud de Broissia's version
self.power = 0.0015
self.low_state = np.array([self.min_position, -self.max_speed])
self.high_state = np.array([self.max_position, self.max_speed])
self.viewer = None
self.action_space = spaces.Box(self.min_action, self.max_action, shape = (1,))
self.observation_space = spaces.Box(self.low_state, self.high_state)
self._seed()
self.reset()
def __init__(self):
mujoco_env.MujocoEnv.__init__(self, 'hopper.xml', 4)
utils.EzPickle.__init__(self)
## Adversarial setup
self._adv_f_bname = b'foot' #Byte String name of body on which the adversary force will be applied
bnames = self.model.body_names
self._adv_bindex = bnames.index(self._adv_f_bname) #Index of the body on which the adversary force will be applied
adv_max_force = 5.0
high_adv = np.ones(2)*adv_max_force
low_adv = -high_adv
self.adv_action_space = spaces.Box(low_adv, high_adv)
self.pro_action_space = self.action_space
mass_ind = self.model.body_names.index(b'torso')
me = np.array(self.model.body_mass)
me[mass_ind,0] = 6.0
self.model.body_mass = me
def __init__(self):
mujoco_env.MujocoEnv.__init__(self, 'hopper.xml', 4)
utils.EzPickle.__init__(self)
## Adversarial setup
self._adv_f_bname = [b'foot', b'torso'] #Byte String name of body on which the adversary force will be applied
bnames = self.model.body_names
self._adv_bindex = [bnames.index(i) for i in self._adv_f_bname] #Index of the body on which the adversary force will be applied
adv_max_force = 5.0
high_adv = np.ones(2*len(self._adv_bindex))*adv_max_force
low_adv = -high_adv
self.adv_action_space = spaces.Box(low_adv, high_adv)
self.pro_action_space = self.action_space
mass_ind = self.model.body_names.index(b'torso')
me = np.array(self.model.body_mass)
me[mass_ind,0] = 6.0
self.model.body_mass = me
def __init__(self):
self.min_position = -1.2
self.max_position = 0.6
self.max_speed = 0.07
self.goal_position = 0.5
self.init_red = 0.0025
self.low = np.array([self.min_position, -self.max_speed])
self.high = np.array([self.max_position, self.max_speed])
self.viewer = None
self.pro_action_space = spaces.Discrete(3)
# Adversarial space is continuous on gravity here
grav_change_abs = np.array([0.0025])
self.adv_action_space = spaces.Box(-grav_change_abs,grav_change_abs)
self.observation_space = spaces.Box(self.low, self.high)
self._seed()
self.reset()
def __init__(self):
self.min_action = -1.0
self.max_action = 1.0
self.min_position = -1.2
self.max_position = 0.6
self.max_speed = 0.07
self.goal_position = 0.45 # was 0.5 in gym, 0.45 in Arnaud de Broissia's version
self.power = 0.0015
self.init_red = 0.0025
self.low_state = np.array([self.min_position, -self.max_speed])
self.high_state = np.array([self.max_position, self.max_speed])
self.viewer = None
self.pro_action_space = spaces.Box(self.min_action, self.max_action, shape = (1,))
# Adversarial space is continuous on gravity here
grav_change_abs = np.array([0.0025])
self.adv_action_space = spaces.Box(-grav_change_abs,grav_change_abs)
self.observation_space = spaces.Box(self.low_state, self.high_state)
self._seed()
self.reset()
atari_wrappers_deprecated.py 文件源码
项目:distributional_perspective_on_RL
作者: Kiwoo
项目源码
文件源码
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def __init__(self, env=None):
super(ProcessFrame84, self).__init__(env)
self.observation_space = spaces.Box(low=0, high=255, shape=(84, 84, 1))
atari_wrappers_deprecated.py 文件源码
项目:distributional_perspective_on_RL
作者: Kiwoo
项目源码
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def __init__(self, env, k):
"""Stack k last frames.
Returns lazy array, which is much more memory efficient.
See Also
--------
baselines.common.atari_wrappers.LazyFrames
"""
gym.Wrapper.__init__(self, env)
self.k = k
self.frames = deque([], maxlen=k)
shp = env.observation_space.shape
self.observation_space = spaces.Box(low=0, high=255, shape=(shp[0], shp[1], shp[2] * k))
atari_wrappers_deprecated.py 文件源码
项目:distributional_perspective_on_RL
作者: Kiwoo
项目源码
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def __init__(self, env):
gym.Wrapper.__init__(self, env)
self.observation_space = spaces.Box(low=0, high=255, shape=(84, 84, 1))
def __init__(self):
# Angle at which to fail the episode
self.theta_threshold_radians = 12 * 2 * math.pi / 360
self.x_threshold = 2.4
# Initializing Course : predfined Oval Course
# ToDo: ????????????
Rad = 190.0
Poly = 16
self.Course = Walls(240, 50, 640-(50+Rad),50)
for i in range(1, Poly):
self.Course.addPoint(Rad*math.cos(-np.pi/2.0 + np.pi*i/Poly)+640-(50+Rad),
Rad*math.sin(-np.pi/2.0 + np.pi*i/Poly)+50+Rad)
self.Course.addPoint(240, 50+Rad*2)
for i in range(1, Poly):
self.Course.addPoint(Rad*math.cos(np.pi/2.0 + np.pi*i/Poly)+(50+Rad),
Rad*math.sin(np.pi/2.0 + np.pi*i/Poly)+50+Rad)
self.Course.addPoint(240,50)
# Outr Boundary Box
self.BBox = Walls(640, 479, 0, 479)
self.BBox.addPoint(0,0)
self.BBox.addPoint(640,0)
self.BBox.addPoint(640,479)
# Mono Sensor Line Follower
self.A = Agent((640, 480), 240, 49)
# Action Space : left wheel speed, right wheel speed
# Observation Space : Detect Line (True, False)
self.action_space = spaces.Box( np.array([-1.,-1.]), np.array([+1.,+1.]))
self.observation_space = spaces.Discrete(1)
self._seed()
self.reset()
self.viewer = None
self.steps_beyond_done = None
self._configure()
def __init__(self, env=None):
super(ProcessFrame84, self).__init__(env)
self.observation_space = spaces.Box(low=0, high=255, shape=(84, 84, 1))
def __init__(self, env):
"""Warp frames to 84x84 as done in the Nature paper and later work."""
gym.ObservationWrapper.__init__(self, env)
self.width = 84
self.height = 84
self.observation_space = spaces.Box(low=0, high=255, shape=(self.height, self.width, 1))
def __init__(self, env, k):
"""Stack k last frames.
Returns lazy array, which is much more memory efficient.
See Also
--------
baselines.common.atari_wrappers.LazyFrames
"""
gym.Wrapper.__init__(self, env)
self.k = k
self.frames = deque([], maxlen=k)
shp = env.observation_space.shape
self.observation_space = spaces.Box(low=0, high=255, shape=(shp[0], shp[1], shp[2] * k))
atari_wrappers_deprecated.py 文件源码
项目:combine-DT-with-NN-in-RL
作者: Burning-Bear
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def __init__(self, env=None):
super(ProcessFrame84, self).__init__(env)
self.observation_space = spaces.Box(low=0, high=255, shape=(84, 84, 1))
atari_wrappers_deprecated.py 文件源码
项目:combine-DT-with-NN-in-RL
作者: Burning-Bear
项目源码
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def __init__(self, env, k):
"""Stack k last frames.
Returns lazy array, which is much more memory efficient.
See Also
--------
baselines.common.atari_wrappers.LazyFrames
"""
gym.Wrapper.__init__(self, env)
self.k = k
self.frames = deque([], maxlen=k)
shp = env.observation_space.shape
self.observation_space = spaces.Box(low=0, high=255, shape=(shp[0], shp[1], shp[2] * k))
atari_wrappers_deprecated.py 文件源码
项目:combine-DT-with-NN-in-RL
作者: Burning-Bear
项目源码
文件源码
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def __init__(self, env):
gym.Wrapper.__init__(self, env)
self.observation_space = spaces.Box(low=0, high=255, shape=(84, 84, 1))
atari_wrappers_deprecated.py 文件源码
项目:combine-DT-with-NN-in-RL
作者: Burning-Bear
项目源码
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def __init__(self, env=None):
super(ProcessFrame84, self).__init__(env)
self.observation_space = spaces.Box(low=0, high=255, shape=(84, 84, 1))
atari_wrappers_deprecated.py 文件源码
项目:combine-DT-with-NN-in-RL
作者: Burning-Bear
项目源码
文件源码
阅读 21
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def __init__(self, env, k):
"""Stack k last frames.
Returns lazy array, which is much more memory efficient.
See Also
--------
baselines.common.atari_wrappers.LazyFrames
"""
gym.Wrapper.__init__(self, env)
self.k = k
self.frames = deque([], maxlen=k)
shp = env.observation_space.shape
self.observation_space = spaces.Box(low=0, high=255, shape=(shp[0], shp[1], shp[2] * k))
def __init__(self, gravity=9.8, masscart=1.0, masspole=0.1, length = .5, force_mag = 10.0):
self.gravity = gravity
self.masscart = masscart
self.masspole = masspole
self.total_mass = (self.masspole + self.masscart)
self.length = length # actually half the pole's length
self.polemass_length = (self.masspole * self.length)
self.force_mag = force_mag
self.tau = 0.02 # seconds between state updates
# Angle at which to fail the episode
self.theta_threshold_radians = 12 * 2 * math.pi / 360
self.x_threshold = 2.4
# Angle limit set to 2 * theta_threshold_radians so failing observation is still within bounds
high = np.array([
self.x_threshold * 2,
np.finfo(np.float32).max,
self.theta_threshold_radians * 2,
np.finfo(np.float32).max])
self.action_space = spaces.Discrete(2)
self.observation_space = spaces.Box(-high, high)
self._seed()
self.viewer = None
self.state = None
self.steps_beyond_done = None
def action_space(self):
if isinstance(self._wrapped_env.action_space, Box):
ub = np.ones(self._wrapped_env.action_space.shape)
return spaces.Box(-1 * ub, ub)
return self._wrapped_env.action_space