def _reset_env(self, env: EnvManager) -> None:
"""Resets the environment.
Returns:
A Data structure corresponding to the initial reset state of the
environment.
"""
new_config = self.param_manager.get_current_samplers()
env.reset(config=new_config)
def _reset_env(self, env: EnvManager) -> None:
"""Resets the environment.
Returns:
A Data structure corresponding to the initial reset state of the
environment.
"""
new_meta_curriculum_config = (self.meta_curriculum.get_config()
if self.meta_curriculum else None)
env.reset(config=new_meta_curriculum_config)
def _reset_env(self, env_manager: EnvManager) -> None:
"""Resets the environment.
Returns:
A Data structure corresponding to the initial reset state of the
environment.
"""
new_config = self.param_manager.get_current_samplers()
env_manager.reset(config=new_config)
# Register any new behavior ids that were generated on the reset.
self._register_new_behaviors(env_manager, env_manager.first_step_infos)
def _reset_env(self, env_manager: EnvManager) -> None:
"""Resets the environment.
Returns:
A Data structure corresponding to the initial reset state of the
environment.
"""
sampled_reset_param = self.sampler_manager.sample_all()
new_meta_curriculum_config = (self.meta_curriculum.get_config()
if self.meta_curriculum else {})
sampled_reset_param.update(new_meta_curriculum_config)
env_manager.reset(config=sampled_reset_param)