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)