def action_space(self):
return Box(low=-abs(self.env_params["max_break"]),
high=self.env_params["max_torque"],
shape=(self.num_trucks - 1, ))
def __init__(self, env=None):
super(FlashRescale, self).__init__(env)
self.observation_space = Box(0, 255, [128, 200, 1])
def action_space(self):
num_actions = self.initial_vehicles.num_rl_vehicles
accel_ub = self.env_params.additional_params["max_accel"]
accel_lb = -abs(self.env_params.additional_params["max_decel"])
return Box(low=accel_lb, high=accel_ub, shape=(num_actions, ))
def __init__(self, env, env_conf):
gym.ObservationWrapper.__init__(self, env)
self.observation_space = Box(0.0, 1.0, [1, 80, 80])
self.conf = env_conf
def __init__(self, env=None):
super(MyAtariRescale42x42, self).__init__(env)
self.observation_space = Box(0.0, 1.0, [1, 42, 42])
def __init__(self, full_episode=False):
super(CarRacingWrapper, self).__init__()
self.full_episode = full_episode
self.observation_space = Box(low=0,
high=255,
shape=(SCREEN_X, SCREEN_Y, 3))
def action_space(self):
"""See class definition."""
return Box(low=-abs(self.env_params.additional_params["max_decel"]),
high=self.env_params.additional_params["max_accel"],
shape=(self.num_rl, ),
dtype=np.float32)
def observation_space(self):
return Box(low=-1, high=1, shape=(7 * self.num_rl, ), dtype=np.float32)
def observation_space(self):
return Box(low=float('-inf'),
high=float('inf'),
shape=(6 * self.num_rl, ),
dtype=np.float32)
def __init__(self, env, env_conf):
gym.ObservationWrapper.__init__(self, env)
self.observation_space = Box(0.0, 1.0, [80, 80, 1], dtype=np.uint8)
self.conf = env_conf
def observation_space(self):
return Box(low=0,
high=255,
shape=(self.size, self.size, self.frame_skip),
dtype=np.uint8)
def observation_space(self):
"""See class definition."""
return Box(low=-float("inf"),
high=float("inf"),
shape=(1, ),
dtype=np.float32)
def action_space(self):
return Box(low=0,
high=1,
shape=(self.num_traffic_lights, ),
dtype=np.float32)
def observation_space(self):
return Box(low=0, high=1, shape=(3 * self.num_trucks - 1,))
def observation_space(self):
return Box(low=-float("inf"), high=float("inf"), shape=(1,),
dtype=np.float32)
def box():
return Box(low=np.array([1.2, 256, -8, -2]),
high=np.array([1.5, 1024, -4, 8]))
def __init__(self, env=None):
super(WrapPyTorch, self).__init__(env)
self.observation_space = Box(0.0, 1.0, [1, 84, 84])
def __init__(self, env=None, obs_height=160, obs_width=160):
super(WobRescale, self).__init__(env)
self.obs_height = obs_height
self.obs_width = obs_width
self.observation_space = Box(0.0, 1.0, [obs_height, obs_width, 1])
def observation_space(self):
"""See class definition."""
return Box(low=-1, high=1, shape=(5 * self.num_rl, ), dtype=np.float32)
def observation_space(self):
"""See class definition."""
return Box(low=0, high=1, shape=(3,), dtype=np.float32)
def __init__(self, env=None):
super(AddTimestep, self).__init__(env)
self.observation_space = Box(self.observation_space.low[0],
self.observation_space.high[0],
[self.observation_space.shape[0] + 1],
dtype=self.observation_space.dtype)
def observation_space(self):
# Return the observation space adjusted to match the shape of the processed
# observations.
c = 1 if self.gray_scale else 3
shape = self.image_size + (c, )
return Box(low=0, high=255, shape=shape, dtype=np.uint8)
def __init__(self, env=None):
super(AtariProcessing, self).__init__(env)
self.observation_space = Box(0.0, 1.0, [42, 42, 1])
def __init__(self, env=None):
super(AtariRescale84x84Env, self).__init__(env)
self.observation_space = Box(0, 255, [84, 84, 1])
def __init__(self, env=None):
super(AtariRescale42x42, self).__init__(env)
self.observation_space = Box(0, 255, [42, 42, 1])
def __init__(self, env, env_conf):
gym.ObservationWrapper.__init__(self, env)
self.observation_space = Box(0, 255, [1, 84, 84], dtype=np.uint8)
self.conf = env_conf
env = MixedEnv()
env.seed(100)
return env
return _thunk
return SubprocVecEnv([make_env()])
if __name__ == '__main__':
sess = tf_util.make_session()
restores = []
navigation_model = LstmPolicy(sess,
Box(low=0,
high=255,
shape=(84, 84, 12),
dtype=np.uint8),
Discrete(3),
1,
1,
reuse=False,
model_name='navi')
navigation_params = find_trainable_variables('navi')
navigation_loaded = joblib.load(
'O:\\Doom\\a2c\\scenarios\\display\\navi.dat')
for p, loaded_p in zip(navigation_params, navigation_loaded):
restores.append(p.assign(loaded_p))
shoot_model = LstmPolicy(sess,
Box(low=0,
high=255,
def __init__(self, env=None):
super(ScaleObservations, self).__init__(env)
self.obs_lo = self.observation_space.low[0,0,0]
self.obs_hi = self.observation_space.high[0,0,0]
obs_shape = self.observation_space.shape
self.observation_space = Box(0.0, 1.0, obs_shape, dtype=np.float32)
def observation_space(self):
return Box(
low=0,
high=float("inf"),
shape=(2 * self.initial_vehicles.num_vehicles, ),
)
def __init__(self, env=None):
super(AtariRescale84x84, self).__init__(env)
self.observation_space = Box(0.0, 1.0, [3, 84, 84])
