random metric works first results

src/active_bo_msgs/msg/ActiveRL.msg

@@ -1,4 +1,5 @@
 string env
 uint32 seed
+bool final_run
 float32[] policy
 float32[] weights

src/active_bo_ros/active_bo_ros/BayesianOptimization/BayesianOptimization.py

@@ -21,7 +21,6 @@ class BayesianOptimization:
         self.episode = 0
         self.counter_array = np.empty((1, 1))
         self.best_reward = np.empty((1, 1))
-        self.distance_penalty = 0
 
         self.nr_policy_weights = nr_weights
         self.nr_steps = nr_steps
@@ -63,8 +62,6 @@ class BayesianOptimization:
                 break
 
             if not done and i == len(policy):
-                distance = -(self.env.goal_position - output[0][0])
-                env_reward += distance * self.distance_penalty
                 self.counter_array = np.vstack((self.counter_array, step_count))
 
         self.env.reset(seed=seed)
@@ -157,11 +154,11 @@ class BayesianOptimization:
         self.episode += 1
 
     def get_best_result(self):
-        y_hat = self.GP.predict(self.X)
+        y_max = np.max(self.Y)
-        idx = np.argmax(y_hat)
+        idx = np.argmax(self.Y)
         x_max = self.X[idx, :]
 
         self.policy_model.weights = x_max
         best_policy = self.policy_model.rollout().reshape(-1,)
 
-        return best_policy, y_hat[idx], x_max
+        return best_policy, y_max, x_max

src/active_bo_ros/active_bo_ros/active_bo_topic.py

@@ -189,17 +189,52 @@ class ActiveBOTopic(Node):
                 bo_response.reward_std = np.std(self.reward, axis=1).tolist()
 
                 if self.save_result:
+                    if self.bo_env == "Mountain Car":
+                        env = 'mc'
+                    elif self.bo_env == "Cartpole":
+                        env = 'cp'
+                    elif self.bo_env == "Acrobot":
+                        env = 'ab'
+                    elif self.bo_env == "Pendulum":
+                        env = 'pd'
+                    else:
+                        raise NotImplementedError
+
+                    if self.bo_acq_fcn == "Expected Improvement":
+                        acq = 'ei'
+                    elif self.bo_acq_fcn == "Probability of Improvement":
+                        acq = 'pi'
+                    elif self.bo_acq_fcn == "Upper Confidence Bound":
+                        acq = 'cb'
+                    else:
+                        raise NotImplementedError
+
                     home_dir = os.path.expanduser('~')
                     file_path = os.path.join(home_dir, 'Documents/IntRLResults')
-                    filename = self.bo_metric + '-'\
+                    filename = env + '-' + acq + '-' + self.bo_metric + '-' \
                                + str(self.bo_metric_parameter) + '-' \
                                + str(self.bo_nr_weights) + '-' + str(time.time())
                     filename = filename.replace('.', '_') + '.csv'
                     path = os.path.join(file_path, filename)
-                    self.get_logger().info(path)
 
                     np.savetxt(path, self.reward, delimiter=',')
 
+                active_rl_request = ActiveRL()
+
+                if self.seed is None:
+                    seed = int(np.random.randint(1, 2147483647, 1)[0])
+                else:
+                    seed = self.seed
+
+                active_rl_request.env = self.bo_env
+                active_rl_request.seed = seed
+                active_rl_request.policy = self.best_policy[:, best_policy_idx].tolist()
+                active_rl_request.weights = self.best_weights[:, best_policy_idx].tolist()
+                active_rl_request.final_run = True
+
+                self.get_logger().info('Calling: Active RL')
+                self.active_rl_pub.publish(active_rl_request)
+
                 self.get_logger().info('Responding: Active BO')
                 self.active_bo_pub.publish(bo_response)
                 self.reset_bo_request()
@@ -245,6 +280,7 @@ class ActiveBOTopic(Node):
                             active_rl_request.seed = seed
                             active_rl_request.policy = old_policy.tolist()
                             active_rl_request.weights = old_weights.tolist()
+                            active_rl_request.final_run = False
 
                             self.get_logger().info('Calling: Active RL')
                             self.active_rl_pub.publish(active_rl_request)
@@ -282,6 +318,7 @@ class ActiveBOTopic(Node):
             state_msg.last_user_reward = self.rl_reward
             self.state_pub.publish(state_msg)
 
+
 def main(args=None):
     rclpy.init(args=args)
 

src/active_bo_ros/active_bo_ros/active_rl_topic.py

@@ -69,6 +69,7 @@ class ActiveRLService(Node):
         self.best_pol_shown = False
         self.policy_sent = False
         self.active_rl_pending = False
+        self.final_run = False
 
         # Main loop timer object
         self.mainloop_timer_period = 0.05
@@ -87,6 +88,7 @@ class ActiveRLService(Node):
         self.rl_seed = msg.seed
         self.rl_policy = np.array(msg.policy, dtype=np.float32)
         self.rl_weights = msg.weights
+        self.final_run = msg.final_run
 
         if self.rl_env == "Mountain Car":
             self.env = Continuous_MountainCarEnv(render_mode="rgb_array")
@@ -151,6 +153,7 @@ class ActiveRLService(Node):
 
     def mainloop_callback(self):
         if self.active_rl_pending:
+            if not self.final_run:
                 if not self.best_pol_shown:
                     if not self.policy_sent:
                         self.rl_step = 0
@@ -201,6 +204,29 @@ class ActiveRLService(Node):
                             self.best_pol_shown = False
                             self.eval_response_received = False
                             self.active_rl_pending = False
+            else:
+                if not self.policy_sent:
+                    self.rl_step = 0
+                    self.rl_reward = 0.0
+                    self.env.reset(seed=self.rl_seed)
+
+                    eval_request = ActiveRL()
+                    eval_request.policy = self.rl_policy.tolist()
+                    eval_request.weights = self.rl_weights
+
+                    self.eval_pub.publish(eval_request)
+                    self.get_logger().info('Active RL: Called!')
+                    self.get_logger().info('Active RL: Waiting for Eval!')
+
+                    self.policy_sent = True
+
+                done = self.next_image(self.rl_policy)
+
+                if done:
+                    self.rl_step = 0
+                    self.rl_reward = 0.0
+                    self.final_run = False
+                    self.active_rl_pending = False
 
 
 def main(args=None):