def __init__(self, env):
ObservationWrapper.__init__(self, env)
# Important so that gym's play.py picks up the right resolution
self.observation_space = spaces.Box(low=0,
high=255,
shape=(84, 84),
dtype=np.uint8)
def __init__(self, env, color=False):
"""A gym wrapper that crops, scales image into the desired shapes and optionally grayscales it."""
ObservationWrapper.__init__(self, env)
self.color = color
self.img_size = (1, 64, 64)
self.observation_space = Box(0.0, 1.0, (1, 64, 64))
def __init__(self, env, height=42, width=42, margins=[1, 1, 1, 1]):
"""A gym wrapper that crops, scales image into the desired shapes and grayscales it."""
ObservationWrapper.__init__(self, env)
self.img_size = (1, width, height)
self.desired_img_size = (width, height)
self.margins = margins
self.observation_space = Box(0.0, 1.0, self.img_size)
def __init__(self, env):
ObservationWrapper.__init__(self, env)
# Important so that gym's play.py picks up the right resolution
obs_shape = env.observation_space.shape
assert len(obs_shape) == 3 # height, width, n_stack
self.observation_space = spaces.Box(low=0, high=255,
shape=(obs_shape[0], obs_shape[1] * obs_shape[2]),
dtype=np.uint8)
def __init__(self, env, reset_listener):
ObservationWrapper.__init__(self, env=env)
self.reset_listener = reset_listener
self.last_e_steps = 0
self.episodes = 1
self.observations_list = []
def __init__(self, env):
"""
preprocess environment observation shape
"""
ObservationWrapper.__init__(self, env)
self.shape = (60, 60)
self.observation_space = Box(0.0, 1.0,
(self.shape[0], self.shape[1], 1))
def __init__(self, env, model_dir, save_frequency_steps):
ObservationWrapper.__init__(self, env=env)
self.save_frequency_steps = save_frequency_steps
self.total_steps = 0
self.total_steps_save_counter = 0
self.total_episodes = 0
self.model_dir = model_dir
def __init__(self, env, model_dir, save_frequency_steps):
ObservationWrapper.__init__(self, env=env)
self.save_frequency_steps = save_frequency_steps
self.total_steps = 0
self.total_steps_save_counter = 0
self.total_episodes = 0
self.str_time_start = time.strftime("%Y%m%d-%H%M")
self.model_dir = model_dir
def __init__(self, env, img_size = (84, 84, 1)):
ObservationWrapper.__init__(self, env)
self.img_size = img_size
if not self.img_size:
self.img_size = (env.observation_space.shape[0],
env.observation_space.shape[1],
1)
self.observation_space = Box(0.0, 1.0, self.img_size
, dtype=np.float32)
def __init__(self, env):
ObservationWrapper.__init__(self, env)
self.frames_since_reset = None
def __init__(self, env):
ObservationWrapper.__init__(self, env)
self.observation_space = spaces.Box(low=0.0,
high=1.0,
shape=env.observation_space.shape,
dtype=np.float32)
def __init__(self, env):
"""A gym wrapper that crops, scales image into the desired shapes and grayscales it."""
ObservationWrapper.__init__(self, env)
self.image_size = (1, 64, 64)
self.observation_space = Box(0.0, 1.0, self.image_size)
def __init__(self, env):
ObservationWrapper.__init__(self, env)
self.unwrapped.observation_space = Box(-np.inf, np.inf, (7, ))
def __init__(self, env):
"""A gym wrapper that crops, scales image into the desired shapes and optionally grayscales it."""
ObservationWrapper.__init__(self, env)
self.img_size = (64, 64)
self.observation_space = Box(0.0, 1.0, (self.img_size[0], self.img_size[1], 1))