added _backup_result to keep track of the result even if the algorith…

…m continues to search for solutions with shorter pulses

src/qutip_qoc/_rl.py

@@ -88,7 +88,19 @@ def create_pulse_func(idx):
             n_iters = 0,                            # Number of iterations(episodes) until convergence 
             iter_seconds = [],                      # list containing the time taken for each iteration(episode) of the optimization
             var_time = True,                        # Whether the optimization was performed with variable time
+            guess_params=[]
         )
+
+        self._backup_result = Result(                              # used as a backup in case the algorithm with shorter_pulses does not find an episode with infid<target_infid
+            objectives = objectives,
+            time_interval = time_interval,
+            start_local_time = time.localtime(),
+            n_iters = 0,
+            iter_seconds = [],
+            var_time = True,
+            guess_params=[]
+        )
+        self._use_backup_result = False                             # if true, use self._backup_result as the final optimization result
 
         #for the reward
         self._step_penalty = 1
@@ -228,21 +240,39 @@ def reset(self, seed=None):
         self._state = self._initial
         return self._get_obs(), {}
 
-    def result(self):
+    def _save_result(self):
         """
-        Retrieve the results of the optimization process, including the optimized 
+        Save the results of the optimization process, including the optimized 
         pulse sequences, final states, and performance metrics.
         """
-        self._result.end_local_time = time.localtime()
-        self._result.n_iters = len(self._result.iter_seconds)  
-        self._result.optimized_params = self._actions.copy() + [self._result.total_seconds]                    # If var_time is True, the last parameter is the evolution time
-        self._result._optimized_controls = self._actions.copy()
-        self._result.start_local_time = time.strftime("%Y-%m-%d %H:%M:%S", self._result.start_local_time)       # Convert to a string
-        self._result.end_local_time = time.strftime("%Y-%m-%d %H:%M:%S", self._result.end_local_time)           # Convert to a string
-        self._result._guess_controls = []
-        self._result._optimized_H = [self._H]
-        self._result.guess_params = []
-        return self._result
+        result_obj = self._backup_result if self._use_backup_result else self._result
+
+        if(self._use_backup_result):
+            self._backup_result.iter_seconds = self._result.iter_seconds.copy()
+            self._backup_result._final_states = self._result._final_states.copy()
+            self._backup_result.infidelity = self._result.infidelity
+
+        result_obj.end_local_time = time.localtime()
+        result_obj.n_iters = len(self._result.iter_seconds)  
+        result_obj.optimized_params = self._actions.copy() + [self._result.total_seconds]                      # If var_time is True, the last parameter is the evolution time
+        result_obj._optimized_controls = self._actions.copy()
+        result_obj._guess_controls = []
+        result_obj._optimized_H = [self._H]
+
+
+    def result(self):
+        """
+        Final conversions and return of optimization results
+        """            
+        if self._use_backup_result:
+            self._backup_result.start_local_time = time.strftime("%Y-%m-%d %H:%M:%S", self._backup_result.start_local_time)       # Convert to a string
+            self._backup_result.end_local_time = time.strftime("%Y-%m-%d %H:%M:%S", self._backup_result.end_local_time)           # Convert to a string
+            return self._backup_result
+        else:
+            self._save_result()
+            self._result.start_local_time = time.strftime("%Y-%m-%d %H:%M:%S", self._result.start_local_time)       # Convert to a string
+            self._result.end_local_time = time.strftime("%Y-%m-%d %H:%M:%S", self._result.end_local_time)           # Convert to a string
+            return self._result
 
     def train(self):
         """
@@ -266,7 +296,6 @@ class EarlyStopTraining(BaseCallback):
     """
     def __init__(self, verbose: int = 0):
         super(EarlyStopTraining, self).__init__(verbose)
-        self.stop_train = False
 
     def _on_step(self) -> bool:
         """
@@ -279,12 +308,18 @@ def _on_step(self) -> bool:
 
         # Check if we need to stop training
         if env.current_episode >= env.max_episodes:
-            env._result.message = f"Reached {env.max_episodes} episodes, stopping training."
+            if env._use_backup_result == True:
+                env._backup_result.message = f"Reached {env.max_episodes} episodes, stopping training. Return the last founded episode with infid < target_infid"
+            else:
+                env._result.message = f"Reached {env.max_episodes} episodes, stopping training."
             return False    # Stop training
         elif (env._result.infidelity <= env._fid_err_targ) and not(env.shorter_pulses):
             env._result.message = f"Stop training because an episode with infidelity <= target infidelity was found"
             return False  # Stop training
         elif env.shorter_pulses:
+            if(env._result.infidelity <= env._fid_err_targ):        # if it finds an episode with infidelity lower than target infidelity, I'll save it in the meantime
+                env._use_backup_result = True
+                env._save_result()
             if len(env._episode_info) >= 100:
                 last_100_episodes = env._episode_info[-100:]
 
@@ -293,6 +328,7 @@ def _on_step(self) -> bool:
                 infid_condition = all(ep['final_infidelity'] <= env._fid_err_targ for ep in last_100_episodes)
 
                 if steps_condition and infid_condition:
+                    env._use_backup_result = False
                     env._result.message = "Training finished. No episode in the last 100 used fewer steps and infidelity was below target infid."
                     return False  # Stop training
         return True  # Continue training

tests/test_result.py

@@ -190,6 +190,18 @@ def sin_z_jax(t, r, **kwargs):
 
 unitary_rl = state2state_rl._replace(
     objectives=[Objective(initial, H, target)],
+        control_parameters = {
+        "p": {"bounds": [(-13, 13)]},
+        "__time__": {
+            "guess": np.array([0.0]),  #dummy value
+            "bounds": [(0.0, 0.0)]    #dummy value
+        }
+    },
+    algorithm_kwargs={
+        "fid_err_targ": 0.01,
+        "alg": "RL",
+        "max_iter": 300,
+    },
 )