Adding basic id to PPO instances to allow for parallel training

Federico-PizarroBejarano · Nov 19, 2024 · 64c5337 · 64c5337
1 parent 43f0f44
commit 64c5337
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Showing 2 changed files with 6 additions and 5 deletions.
diff --git a/safe_control_gym/controllers/ppo/ppo.py b/safe_control_gym/controllers/ppo/ppo.py
@@ -90,6 +90,7 @@ 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.'''
@@ -307,7 +308,7 @@ 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('./temp-data/saved_controller_prev.npy', save_only_random_seed=True)
+            self.save(f'./temp-data/saved_controller_prev_{self.instance_idx}.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.npy', save_only_random_seed=True)
-            self.uncertified_controller.load(f'{self.output_dir}/temp-data/saved_controller_prev.npy', load_only_random_seed=True)
+            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)
 
         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.npy', load_only_random_seed=True)
-            self.uncertified_controller.save(f'{self.output_dir}/temp-data/saved_controller_prev.npy', save_only_random_seed=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)
 
         return v_L