integrating the metrics

src/active_bo_ros/active_bo_ros/active_bo_topic.py

@@ -15,6 +15,11 @@ from active_bo_ros.ReinforcementLearning.CartPole import CartPoleEnv
 from active_bo_ros.ReinforcementLearning.Pendulum import PendulumEnv
 from active_bo_ros.ReinforcementLearning.Acrobot import AcrobotEnv
 
+from active_bo_ros.UserQuery.random_query import RandomQuery
+from active_bo_ros.UserQuery.regular_query import RegularQuery
+from active_bo_ros.UserQuery.improvement_query import ImprovementQuery
+from active_bo_ros.UserQuery.max_acq_query import MaxAcqQuery
+
 import numpy as np
 import time
 
@@ -39,6 +44,7 @@ class ActiveBOTopic(Node):
 
         self.active_bo_pending = False
         self.bo_env = None
+        self.bo_metric = None
         self.bo_fixed_seed = False
         self.bo_nr_weights = None
         self.bo_steps = None
@@ -82,6 +88,7 @@ class ActiveBOTopic(Node):
 
     def reset_bo_request(self):
         self.bo_env = None
+        self.bo_metric = None
         self.bo_fixed_seed = False
         self.bo_nr_weights = None
         self.bo_steps = None
@@ -97,6 +104,7 @@ class ActiveBOTopic(Node):
             self.get_logger().info('Active Bayesian Optimization request pending!')
             self.active_bo_pending = True
             self.bo_env = msg.env
+            self.bo_metric = msg.metric
             self.bo_fixed_seed = msg.fixed_seed
             self.bo_nr_weights = msg.nr_weights
             self.bo_steps = msg.max_steps
@@ -181,12 +189,24 @@ class ActiveBOTopic(Node):
                         self.get_logger().error(f'Active Reinforcement Learning failed to add new observation: {e}')
                 else:
                     if self.current_episode < self.bo_episodes:
-                        if np.random.uniform(0.0, 1.0, 1) < self.bo_metric_parameter:
+                        # metrics
+                        if self.bo_metric == "RandomQuery":
+                            user_query = RandomQuery(self.bo_metric_parameter)
+
+                        else:
+                            raise NotImplementedError
+
+                        if user_query.query():
                             active_rl_request = ActiveRL()
                             old_policy, _, old_weights = self.BO.get_best_result()
 
+                            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 = self.seed
+                            active_rl_request.seed = seed
                             active_rl_request.policy = old_policy.tolist()
                             active_rl_request.weights = old_weights.tolist()
 

src/active_bo_ros/active_bo_ros/active_rl_topic.py

@@ -35,7 +35,6 @@ class ActiveRLService(Node):
                                                       self.active_rl_callback,
                                                       1, callback_group=rl_callback_group)
 
-        self.active_rl_pending = False
         self.rl_env = None
         self.rl_seed = None
         self.rl_policy = None
@@ -66,7 +65,10 @@ class ActiveRLService(Node):
         # RL Environments
         self.env = None
 
+        # State Machine Variables
         self.best_pol_shown = False
+        self.policy_sent = False
+        self.active_rl_pending = False
 
         # Main loop timer object
         self.mainloop_timer_period = 0.05
@@ -100,6 +102,8 @@ class ActiveRLService(Node):
         self.get_logger().info('Active RL: Called!')
         self.env.reset(seed=self.rl_seed)
         self.active_rl_pending = True
+        self.policy_sent = False
+        self.rl_step = 0
 
     def reset_eval_request(self):
         self.eval_policy = None
@@ -148,9 +152,7 @@ class ActiveRLService(Node):
     def mainloop_callback(self):
         if self.active_rl_pending:
             if not self.best_pol_shown:
-                done = self.next_image(self.rl_policy)
+                if not self.policy_sent:
-
-                if done:
                     self.rl_step = 0
                     self.rl_reward = 0.0
                     self.env.reset(seed=self.rl_seed)
@@ -163,7 +165,13 @@ class ActiveRLService(Node):
                     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.best_pol_shown = True
+                    self.rl_step = 0
 
             elif self.best_pol_shown:
                 if not self.eval_response_received:

src/active_bo_ros/setup.py

@@ -11,7 +11,9 @@ setup(
               package_name + '/PolicyModel',
               package_name + '/ReinforcementLearning',
               package_name + '/AcquisitionFunctions',
-              package_name + '/BayesianOptimization'],
+              package_name + '/BayesianOptimization',
+              package_name + '/UserQuery',
+              ],
     data_files=[
         ('share/ament_index/resource_index/packages',
             ['resource/' + package_name]),