Switching to using model name rather than random id

Federico-PizarroBejarano · Nov 20, 2024 · 86e8345 · 86e8345
1 parent 64c5337
commit 86e8345
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Showing 5 changed files with 8 additions and 7 deletions.
diff --git a/experiments/mpsc/mpsc_experiment.py b/experiments/mpsc/mpsc_experiment.py
@@ -73,6 +73,7 @@ def run(plot=True, training=False, n_episodes=1, n_steps=None, curr_path='.', in
     if config.algo in ['ppo', 'sac', 'safe_explorer_ppo', 'cpo']:
         # Load state_dict from trained.
         ctrl.load(f'{curr_path}/models/rl_models/{model}/model_latest.pt')
+        ctrl.model_name = model
 
         # Remove temporary files and directories
         shutil.rmtree(f'{curr_path}/temp', ignore_errors=True)

diff --git a/experiments/mpsc/train_model.sbatch b/experiments/mpsc/train_model.sbatch
@@ -3,7 +3,7 @@
 #SBATCH --output=/h/pizarrob/safe-control-gym/experiments/mpsc/temp-data/mpsf_reset_%j.out
 #SBATCH --error=/h/pizarrob/safe-control-gym/experiments/mpsc/temp-data/mpsf_reset_%j.err
 #SBATCH --partition=cpu
-#SBATCH -t 12:00:00
+#SBATCH -t 24:00:00
 #SBATCH --cpus-per-task=1
 #SBATCH --mem=16G
 #SBATCH --gres=gpu:0

diff --git a/experiments/mpsc/train_rl.py b/experiments/mpsc/train_rl.py
@@ -53,6 +53,7 @@ def train():
 
     # Setup MPSC.
     if config.algo in ['ppo', 'sac']:
+        ctrl.model_name = config.output_dir.split('/')[-2]
         safety_filter = make(config.safety_filter,
                              env_func,
                              **config.sf_config)

diff --git a/safe_control_gym/controllers/ppo/ppo.py b/safe_control_gym/controllers/ppo/ppo.py
@@ -90,7 +90,6 @@ def __init__(self,
 
         # Adding safety filter
         self.safety_filter = None
-        self.instance_idx = int(np.random.rand() * 1000000)
 
     def reset(self):
         '''Do initializations for training or evaluation.'''
@@ -308,7 +307,7 @@ def train_step(self):
         info = self.info
         start = time.time()
         if self.safety_filter is not None and self.preserve_random_state is True:
-            self.save(f'./temp-data/saved_controller_prev_{self.instance_idx}.npy', save_only_random_seed=True)
+            self.save(f'./temp-data/saved_controller_prev_{self.model_name}.npy', save_only_random_seed=True)
         for _ in range(self.rollout_steps):
             with torch.no_grad():
                 action, v, logp = self.agent.ac.step(torch.FloatTensor(obs).to(self.device))

diff --git a/safe_control_gym/safety_filters/mpsc/mpsc_cost_function/precomputed_cost.py b/safe_control_gym/safety_filters/mpsc/mpsc_cost_function/precomputed_cost.py
@@ -101,8 +101,8 @@ def calculate_unsafe_path(self, obs, uncertified_action, iteration):
             self.uncertified_controller.save(f'{self.output_dir}/temp-data/saved_controller_curr.npy')
             self.uncertified_controller.load(f'{self.output_dir}/temp-data/saved_controller_prev.npy')
         elif isinstance(self.uncertified_controller, PPO) and self.uncertified_controller.curr_training is True and self.uncertified_controller.preserve_random_state:
-            self.uncertified_controller.save(f'{self.output_dir}/temp-data/saved_controller_curr_{self.uncertified_controller.instance_idx}.npy', save_only_random_seed=True)
-            self.uncertified_controller.load(f'{self.output_dir}/temp-data/saved_controller_prev_{self.uncertified_controller.instance_idx}.npy', load_only_random_seed=True)
+            self.uncertified_controller.save(f'{self.output_dir}/temp-data/saved_controller_curr_{self.uncertified_controller.model_name}.npy', save_only_random_seed=True)
+            self.uncertified_controller.load(f'{self.output_dir}/temp-data/saved_controller_prev_{self.uncertified_controller.model_name}.npy', load_only_random_seed=True)
 
         for h in range(self.mpsc_cost_horizon):
             next_step = min(iteration + h, self.env.X_GOAL.shape[0] - 1)
@@ -136,7 +136,7 @@ def calculate_unsafe_path(self, obs, uncertified_action, iteration):
             self.uncertified_controller.load(f'{self.output_dir}/temp-data/saved_controller_curr.npy')
             self.uncertified_controller.save(f'{self.output_dir}/temp-data/saved_controller_prev.npy')
         elif isinstance(self.uncertified_controller, PPO) and self.uncertified_controller.curr_training is True and self.uncertified_controller.preserve_random_state is True:
-            self.uncertified_controller.load(f'{self.output_dir}/temp-data/saved_controller_curr_{self.uncertified_controller.instance_idx}.npy', load_only_random_seed=True)
-            self.uncertified_controller.save(f'{self.output_dir}/temp-data/saved_controller_prev_{self.uncertified_controller.instance_idx}.npy', save_only_random_seed=True)
+            self.uncertified_controller.load(f'{self.output_dir}/temp-data/saved_controller_curr_{self.uncertified_controller.model_name}.npy', load_only_random_seed=True)
+            self.uncertified_controller.save(f'{self.output_dir}/temp-data/saved_controller_prev_{self.uncertified_controller.model_name}.npy', save_only_random_seed=True)
 
         return v_L