Minor changes

experiments/mpsc/mpsc_experiment.py

@@ -31,8 +31,6 @@ def run(plot=False, model='ppo'):
     config.task_config['done_on_violation'] = False
     config.task_config['randomized_init'] = False
 
-    system = 'quadrotor_2D_attitude'
-
     # Create an environment
     env_func = partial(make,
                        config.task,
@@ -76,8 +74,6 @@ def run(plot=False, model='ppo'):
         safety_filter.cost_function.uncertified_controller = ctrl
         safety_filter.cost_function.output_dir = '.'
 
-    safety_filter.load(path=f'./models/mpsc_parameters/{config.safety_filter}_{system}.pkl')
-
     # Run with safety filter
     experiment = BaseExperiment(env, ctrl, safety_filter=safety_filter)
     cert_results, cert_metrics = experiment.run_evaluation(n_episodes=1)

experiments/mpsc/train_rl.py

@@ -8,6 +8,7 @@
 import munch
 import yaml
 
+from safe_control_gym.experiments.base_experiment import BaseExperiment
 from safe_control_gym.safety_filters.mpsc.mpsc_utils import Cost_Function
 from safe_control_gym.utils.configuration import ConfigFactory
 from safe_control_gym.utils.plotting import plot_from_logs
@@ -27,8 +28,6 @@ def train():
 
     shutil.rmtree(config.output_dir, ignore_errors=True)
 
-    system = 'quadrotor_2D_attitude'
-
     set_seed_from_config(config)
     set_device_from_config(config)
 
@@ -38,6 +37,7 @@ def train():
                        output_dir=config.output_dir,
                        **config.task_config
                        )
+    env = env_func()
 
     # Create the controller/control_agent.
     ctrl = make(config.algo,
@@ -58,13 +58,12 @@ def train():
             safety_filter.cost_function.uncertified_controller = ctrl
             safety_filter.cost_function.output_dir = '.'
 
-        safety_filter.load(path=f'./models/mpsc_parameters/{config.safety_filter}_{system}.pkl')
-
         ctrl.safety_filter = safety_filter
 
     # Training.
     start_time = time.time()
-    ctrl.learn()
+    experiment = BaseExperiment(env, ctrl, safety_filter=safety_filter)
+    experiment.launch_training()
     config['logging'] = {'total_learning_time': time.time() - start_time}
     ctrl.close()
     print('Training done.')