added random, regular, max acquisition & improvement metric

src/active_bo_ros/active_bo_ros/BayesianOptimization/BayesianOptimization.py

@@ -86,7 +86,7 @@ class BayesianOptimization:
 
             self.Y[i] = reward
 
-        self.GP = GaussianProcessRegressor(Matern(nu=1.5))
+        self.GP = GaussianProcessRegressor(Matern(nu=1.5, length_scale_bounds=(1e-8, 1e5)), n_restarts_optimizer=5, )
         self.GP.fit(self.X, self.Y)
 
     def next_observation(self):
@@ -147,6 +147,8 @@ class BayesianOptimization:
         self.X = np.vstack((self.X, x_new))
         self.Y = np.vstack((self.Y, reward))
 
+        self.GP.fit(self.X, self.Y)
+
         if self.episode == 0:
             self.best_reward[0] = max(self.Y)
         else:

src/active_bo_ros/active_bo_ros/active_bo_topic.py

@@ -132,12 +132,20 @@ class ActiveBOTopic(Node):
 
     def active_rl_callback(self, msg):
         if self.active_rl_pending:
-            self.get_logger().info('Active Reinforcement Learning response pending!')
+            self.get_logger().info('Active Reinforcement Learning response received!')
-            self.active_rl_pending = False
             self.rl_weights = msg.weights
             self.rl_final_step = msg.final_step
             self.rl_reward = msg.reward
 
+            try:
+                self.BO.add_new_observation(self.rl_reward, self.rl_weights)
+                self.get_logger().info('Active Reinforcement Learning added new observation!')
+            except Exception as e:
+                self.get_logger().error(f'Active Reinforcement Learning failed to add new observation: {e}')
+
+            self.active_rl_pending = False
+            self.reset_rl_response()
+
     def mainloop_callback(self):
         if self.active_bo_pending:
 
@@ -181,18 +189,26 @@ class ActiveBOTopic(Node):
             else:
                 if self.active_rl_pending:
                     pass
-                elif self.rl_weights is not None and not self.active_rl_pending:
-                    try:
-                        self.BO.add_new_observation(self.rl_reward, self.rl_weights)
-                        self.reset_rl_response()
-                    except Exception as e:
-                        self.get_logger().error(f'Active Reinforcement Learning failed to add new observation: {e}')
                 else:
                     if self.current_episode < self.bo_episodes:
                         # metrics
-                        if self.bo_metric == "RandomQuery":
+                        if self.bo_metric == "random":
                             user_query = RandomQuery(self.bo_metric_parameter)
 
+                        elif self.bo_metric == "regular":
+                            user_query = RegularQuery(self.bo_metric_parameter)
+
+                        elif self.bo_metric == "max acquisition":
+                            user_query = MaxAcqQuery(self.bo_metric_parameter,
+                                                     self.BO.GP,
+                                                     100,
+                                                     self.bo_nr_weights,
+                                                     acq=self.bo_acq_fcn,
+                                                     X=self.BO.X)
+
+                        elif self.bo_metric == "improvement":
+                            user_query = ImprovementQuery(self.bo_metric_parameter, 10)
+
                         else:
                             raise NotImplementedError
 
@@ -234,7 +250,6 @@ class ActiveBOTopic(Node):
                         self.get_logger().info(f'Current Run: {self.current_run}')
 
 
-
 def main(args=None):
     rclpy.init(args=args)