def __init__(self, *args, log_scale_limit=2.0, fix_params=False, rand_params=RAND_PARAMS, random_seed=None, fixed_goal=True, max_path_length=0, **kwargs):
"""
Half-Cheetah environment with randomized mujoco parameters
:param log_scale_limit: lower / upper limit for uniform sampling in logspace of base 2
:param random_seed: random seed for sampling the mujoco model params
:param fix_params: boolean indicating whether the mujoco parameters shall be fixed
:param rand_params: mujoco model parameters to sample
"""
assert set(rand_params) <= set(self.RAND_PARAMS_EXTENDED), \
"rand_params must be a subset of " + str(self.RAND_PARAMS_EXTENDED)
self.log_scale_limit = log_scale_limit
self.random_seed = random_seed
self.random_state = np.random.RandomState(random_seed)
self.fix_params = fix_params # can be changed by calling the fix_mujoco_parameters method
self.rand_params = rand_params
self.fixed_goal = fixed_goal
self.parameters_already_fixed = False
self.n_steps = 0
self.reward_range = None
self.metadata = {}
if max_path_length is not None:
self.max_path_length = max_path_length
else:
self.max_path_length = 10**8 #set to a large number
args_all, kwargs_all = get_all_function_arguments(self.__init__, locals())
Serializable.__init__(*args_all, **kwargs_all)
def __init__(self,
*args,
log_scale_limit=2.0,
fix_params=False,
fixed_goal=True,
random_seed=None,
**kwargs):
self.sign = 1
self.first = True
self.penalty_ctrl = 0.01
self.init_geom_size = None
self.init_body_pos = None
self.init_geom_pos = None
args_all, kwargs_all = get_all_function_arguments(
self.__init__, locals())
BaseEnvRandParams.__init__(self,
log_scale_limit=log_scale_limit,
fix_params=fix_params,
rand_params=['geom_size'],
fixed_goal=fixed_goal,
random_seed=random_seed)
MujocoEnv.__init__(self, *args, **kwargs)
Serializable.__init__(*args_all, **kwargs_all)
def __init__(self, *args, log_scale_limit=2.0, fix_params=False, rand_params=BaseEnvRandParams.RAND_PARAMS, random_seed=None, max_path_length=None, **kwargs):
"""
Half-Cheetah environment with randomized mujoco parameters
:param log_scale_limit: lower / upper limit for uniform sampling in logspace of base 2
:param random_seed: random seed for sampling the mujoco model params
:param fix_params: boolean indicating whether the mujoco parameters shall be fixed
:param rand_params: mujoco model parameters to sample
"""
args_all, kwargs_all = get_all_function_arguments(self.__init__, locals())
BaseEnvRandParams.__init__(*args_all, **kwargs_all)
ReacherEnv.__init__(self, *args, **kwargs)
Serializable.__init__(*args_all, **kwargs_all)