def __init__(self,
direction=1,
maze_length=0.6,
sparse_reward=False,
no_reward=False,
episode_length=100,
grayscale=True,
width=64,
height=64):
utils.EzPickle.__init__(self)
self.sparse_reward = sparse_reward
self.no_reward = no_reward
self.max_episode_length = episode_length
self.direction = direction
self.length = maze_length
self.width = width
self.height = height
self.grayscale = grayscale
self.episode_length = 0
model = point_mass_maze(direction=self.direction,
length=self.length,
borders=False)
with model.asfile() as f:
mujoco_env.MujocoEnv.__init__(self, f.name, 5)
if self.grayscale:
self.observation_space = Box(0, 1, shape=(width, height))
else:
self.observation_space = Box(0, 1, shape=(width, height, 3))
def __init__(self, direction=1, maze_length=0.6, sparse_reward=False, no_reward=False, discrete=True, episode_length=100):
utils.EzPickle.__init__(self)
self.sparse_reward = sparse_reward
self.no_reward = no_reward
self.max_episode_length = episode_length
self.direction = direction
self.length = maze_length
self.discrete = discrete # if use discrete initial positions
self.episode_length = 0
self.policy_contexts = None
model = point_mass_maze(direction=self.direction, length=self.length)
with model.asfile() as f:
mujoco_env.MujocoEnv.__init__(self, f.name, 5)
def __init__(self,
direction=1,
maze_length=0.6,
sparse_reward=False,
no_reward=False,
episode_length=100):
utils.EzPickle.__init__(self)
self.sparse_reward = sparse_reward
self.no_reward = no_reward
self.max_episode_length = episode_length
self.direction = direction
self.length = maze_length
self.episode_length = 0
model = point_mass_maze(direction=self.direction, length=self.length)
with model.asfile() as f:
mujoco_env.MujocoEnv.__init__(self, f.name, 5)