def convert_gym_space(space):
if isinstance(space, gym.spaces.Box):
return Box(low=space.low, high=space.high)
elif isinstance(space, gym.spaces.Discrete):
return Discrete(n=space.n)
elif isinstance(space, gym.spaces.Tuple):
return Product([convert_gym_space(x) for x in space.spaces])
else:
raise NotImplementedError
def convert_gym_space(space):
# import IPython; IPython.embed()
if isinstance(space, gym.spaces.Box):
return Box(low=0, high=255, shape=(80, 80))
elif isinstance(space, gym.spaces.Discrete):
return Discrete(n=space.n)
elif isinstance(space, gym.spaces.Tuple):
return Product([convert_gym_space(x) for x in space.spaces])
else:
raise NotImplementedError
def convert_gym_space(space, box_additional_dim=0):
if isinstance(space, gym.spaces.Box):
if box_additional_dim != 0:
low = np.concatenate([space.low, [-np.inf] * box_additional_dim])
high = np.concatenate([space.high, [np.inf] * box_additional_dim])
return Box(low=low, high=high)
return Box(low=space.low, high=space.high)
elif isinstance(space, gym.spaces.Discrete):
return Discrete(n=space.n)
elif isinstance(space, gym.spaces.Tuple):
return Product([convert_gym_space(x) for x in space.spaces])
else:
raise NotImplementedError
def convert_gym_space(space):
if isinstance(space, gym.spaces.Box):
return Box(low=space.low, high=space.high)
elif isinstance(space, gym.spaces.Discrete):
return Discrete(n=space.n)
elif isinstance(space, gym.spaces.Tuple):
return Product([convert_gym_space(x) for x in space.spaces])
elif isinstance(space, list):
# For multiagent envs
return list(map(convert_gym_space, space))
# TODO(cathywu) refactor multiagent envs to use gym.spaces.Tuple
# (may be needed for pickling?)
else:
raise NotImplementedError
def convert_gym_space(space):
"""
Convert a gym.space to an rllab.space
:param space: (obj:`gym.Space`) The Space object to convert
:return: converted rllab.Space object
"""
if isinstance(space, gym.spaces.Box):
return Box(low=space.low, high=space.high)
elif isinstance(space, gym.spaces.Discrete):
return Discrete(n=space.n)
elif isinstance(space, gym.spaces.Tuple):
return Product([convert_gym_space(x) for x in space.spaces])
elif isinstance(space, gym.spaces.Dict):
return Dict(space.spaces)
else:
raise TypeError
def convert_gym_space(space):
if isinstance(space, gym.spaces.Box):
return Box(low=space.low, high=space.high)
elif isinstance(space, gym.spaces.Discrete):
return Discrete(n=space.n)
elif isinstance(space, gym.spaces.Tuple):
return Product([convert_gym_space(x) for x in space.spaces])
# added for robotics enviroments
elif isinstance(space, gym.spaces.Dict):
b_low = np.concatenate((space.spaces["desired_goal"].low,space.spaces["achieved_goal"].low,space.spaces["observation"].low))
b_high = np.concatenate((space.spaces["desired_goal"].high,space.spaces["achieved_goal"].high,space.spaces["observation"].high))
name = Box(low=b_low, high=b_high)
print(name)
return name
# end addition
else:
raise NotImplementedError
