'visual': self.number_observations > 0,
}
return observable
def get_observable_dim(self):
'''Get observable dimensions'''
observable_dim = {
'state': self.state_space_size,
'visual':
'some np array shape, as opposed to what Arthur called size',
}
return observable_dim
# Extend Unity BrainParameters class at runtime to add BrainExt methods
util.monkey_patch(BrainParameters, BrainExt)
class OpenAIEnv:
def __init__(self, spec, env_space, e=0):
self.spec = spec
util.set_attr(self, self.spec)
self.name = self.spec['name']
self.env_space = env_space
self.e = e
self.body_e = None
self.nanflat_body_e = None # nanflatten version of bodies
self.body_num = None
self.u_env = gym.make(self.name)
self.max_timestep = self.max_timestep or self.u_env.spec.tags.get(
'state': self.state_space_size > 0,
'image': self.number_observations > 0,
}
return observable
def get_observable_dim(self):
'''Get observable dimensions'''
observable_dim = {
'state': self.state_space_size,
'image': 'some np array shape, as opposed to what Arthur called size',
}
return observable_dim
# Extend Unity BrainParameters class at runtime to add BrainExt methods
util.monkey_patch(brain.BrainParameters, BrainExt)
def set_gym_space_attr(gym_space):
'''Set missing gym space attributes for standardization'''
if isinstance(gym_space, gym.spaces.Box):
pass
elif isinstance(gym_space, gym.spaces.Discrete):
setattr(gym_space, 'low', 0)
setattr(gym_space, 'high', gym_space.n)
elif isinstance(gym_space, gym.spaces.MultiBinary):
setattr(gym_space, 'low', np.full(gym_space.n, 0))
setattr(gym_space, 'high', np.full(gym_space.n, 2))
elif isinstance(gym_space, gym.spaces.MultiDiscrete):
setattr(gym_space, 'low', np.zeros_like(nvec))
setattr(gym_space, 'high', np.array(gym_space.nvec))
