fixing the parameters is working

src/active_bo_msgs/CMakeLists.txt

@@ -28,7 +28,9 @@ rosidl_generate_interfaces(${PROJECT_NAME}
     "msg/ActiveBORequest.msg"
     "msg/ActiveBOResponse.msg"
     "msg/ActiveRLResponse.msg"
-    "msg/ActiveRL.msg"
+    "msg/ActiveRLRequest.msg"
+    "msg/ActiveRLEvalRequest.msg"
+    "msg/ActiveRLEvalResponse.msg"
     "msg/ImageFeedback.msg"
     "msg/ActiveBOState.msg"
 

src/active_bo_msgs/msg/ActiveRLEvalRequest.msg

@@ -0,0 +1,2 @@
+float64[] policy
+float64[] weights

src/active_bo_msgs/msg/ActiveRLEvalResponse.msg

@@ -0,0 +1,3 @@
+bool[] overwrite_weight
+float64[] policy
+float64[] weights

src/active_bo_msgs/msg/ActiveRLRequest.msg

@@ -2,7 +2,5 @@ string env
 uint32 seed
 bool display_run
 uint8 interactive_run
-bool overwrite_param
-bool[] overwrite_weight
 float64[] policy
 float64[] weights

src/active_bo_msgs/msg/ActiveRLResponse.msg

@@ -1,3 +1,4 @@
 float64[] weights
+bool[] overwrite_weight
 uint16 final_step
 float64 reward

src/active_bo_ros/active_bo_ros/active_rl_topic.py

@@ -1,5 +1,7 @@
-from active_bo_msgs.msg import ActiveRL
+from active_bo_msgs.msg import ActiveRLRequest
 from active_bo_msgs.msg import ActiveRLResponse
+from active_bo_msgs.msg import ActiveRLEvalRequest
+from active_bo_msgs.msg import ActiveRLEvalResponse
 
 from active_bo_msgs.msg import ImageFeedback
 
@@ -19,7 +21,7 @@ import time
 import copy
 
 
-class ActiveRLService(Node):
+class ActiveRL(Node):
     def __init__(self):
         super().__init__('active_rl_service')
         rl_callback_group = ReentrantCallbackGroup()
@@ -30,7 +32,7 @@ class ActiveRLService(Node):
         self.active_rl_pub = self.create_publisher(ActiveRLResponse,
                                                    'active_rl_response',
                                                    1, callback_group=rl_callback_group)
-        self.active_rl_sub = self.create_subscription(ActiveRL,
+        self.active_rl_sub = self.create_subscription(ActiveRLRequest,
                                                       'active_rl_request',
                                                       self.active_rl_callback,
                                                       1, callback_group=rl_callback_group)
@@ -48,11 +50,11 @@ class ActiveRLService(Node):
                                                1, callback_group=topic_callback_group)
 
         # Active RL Evaluation Publisher, Subscriber and Message attributes
-        self.eval_pub = self.create_publisher(ActiveRL,
+        self.eval_pub = self.create_publisher(ActiveRLEvalRequest,
                                               'active_rl_eval_request',
                                               1,
                                               callback_group=topic_callback_group)
-        self.eval_sub = self.create_subscription(ActiveRL,
+        self.eval_sub = self.create_subscription(ActiveRLEvalResponse,
                                                  'active_rl_eval_response',
                                                  self.active_rl_eval_callback,
                                                  1,
@@ -61,6 +63,7 @@ class ActiveRLService(Node):
         self.eval_response_received = False
         self.eval_policy = None
         self.eval_weights = None
+        self.overwrite_weight = None
 
         # RL Environments
         self.env = None
@@ -69,7 +72,7 @@ class ActiveRLService(Node):
         self.best_pol_shown = False
         self.policy_sent = False
         self.rl_pending = False
-        self.interactive_run = False
+        self.interactive_run = 0
         self.display_run = False
 
         # Main loop timer object
@@ -84,6 +87,7 @@ class ActiveRLService(Node):
         self.rl_policy = None
         self.rl_weights = None
         self.interactive_run = 0
+        self.display_run = False
 
     def active_rl_callback(self, msg):
         self.rl_env = msg.env
@@ -117,6 +121,7 @@ class ActiveRLService(Node):
     def active_rl_eval_callback(self, msg):
         self.eval_policy = np.array(msg.policy, dtype=np.float64)
         self.eval_weights = msg.weights
+        self.overwrite_weight = msg.overwrite_weight
 
         self.get_logger().info('Active RL Eval: Responded!')
         self.env.reset(seed=self.rl_seed)
@@ -185,7 +190,7 @@ class ActiveRLService(Node):
                         self.rl_reward = 0.0
                         self.env.reset(seed=self.rl_seed)
 
-                        eval_request = ActiveRL()
+                        eval_request = ActiveRLEvalRequest()
                         eval_request.policy = self.rl_policy.tolist()
                         eval_request.weights = self.rl_weights
 
@@ -214,6 +219,7 @@ class ActiveRLService(Node):
                             rl_response.weights = self.eval_weights
                             rl_response.reward = self.rl_reward
                             rl_response.final_step = self.rl_step
+                            rl_response.overwrite_weight = self.overwrite_weight
 
                             self.active_rl_pub.publish(rl_response)
 
@@ -236,7 +242,7 @@ class ActiveRLService(Node):
                     self.rl_reward = 0.0
                     self.env.reset(seed=self.rl_seed)
 
-                    eval_request = ActiveRL()
+                    eval_request = ActiveRLEvalRequest()
                     eval_request.policy = self.rl_policy.tolist()
                     eval_request.weights = self.rl_weights
 
@@ -260,45 +266,23 @@ class ActiveRLService(Node):
                 rl_response.weights = self.rl_weights
                 rl_response.reward = env_reward
                 rl_response.final_step = step_count
+                if self.overwrite_weight is None:
+                    overwrite_weight = [False] * len(self.rl_weights)
+                else:
+                    overwrite_weight = self.overwrite_weight
+
+                rl_response.overwrite_weight = overwrite_weight
 
                 self.active_rl_pub.publish(rl_response)
 
                 self.reset_rl_request()
                 self.rl_pending = False
 
-                # if not self.policy_sent:
-                #     self.rl_step = 0
-                #     self.rl_reward = 0.0
-                #     self.env.reset(seed=self.rl_seed)
-                #     self.policy_sent = True
-                # done = self.next_image(self.rl_policy, self.display_run)
-                #
-                # if done:
-                #     rl_response = ActiveRLResponse()
-                #     rl_response.weights = self.rl_weights
-                #     rl_response.reward = self.rl_reward
-                #     rl_response.final_step = self.rl_step
-                #
-                #     self.active_rl_pub.publish(rl_response)
-                #     self.end_time = time.time()
-                #     self.get_logger().info(f'RL Time: {self.end_time - self.begin_time}, mode: {self.interactive_run}')
-                #     self.begin_time = None
-                #     self.end_time = None
-                #
-                #     # reset flags and attributes
-                #     self.reset_eval_request()
-                #     self.reset_rl_request()
-                #
-                #     self.rl_step = 0
-                #     self.rl_reward = 0.0
-                #
-                #     self.rl_pending = False
-
 
 def main(args=None):
     rclpy.init(args=args)
 
-    active_rl_service = ActiveRLService()
+    active_rl_service = ActiveRL()
 
     rclpy.spin(active_rl_service)
 

src/active_bo_ros/active_bo_ros/interactive_bo.py

@@ -1,6 +1,6 @@
 from active_bo_msgs.msg import ActiveBORequest
 from active_bo_msgs.msg import ActiveBOResponse
-from active_bo_msgs.msg import ActiveRL
+from active_bo_msgs.msg import ActiveRLRequest
 from active_bo_msgs.msg import ActiveRLResponse
 from active_bo_msgs.msg import ActiveBOState
 
@@ -54,6 +54,7 @@ class ActiveBOTopic(Node):
         self.bo_runs = 0
         self.bo_acq_fcn = None
         self.bo_metric_parameter = None
+        self.bo_metric_parameter_2 = None
         self.current_run = 0
         self.current_episode = 0
         self.seed = None
@@ -61,7 +62,7 @@ class ActiveBOTopic(Node):
         self.save_result = False
 
         # Active Reinforcement Learning Publisher, Subscriber and Message attributes
-        self.active_rl_pub = self.create_publisher(ActiveRL,
+        self.active_rl_pub = self.create_publisher(ActiveRLRequest,
                                                    'active_rl_request',
                                                    1, callback_group=rl_callback_group)
         self.active_rl_sub = self.create_subscription(ActiveRLResponse,
@@ -73,6 +74,7 @@ class ActiveBOTopic(Node):
         self.rl_weights = None
         self.rl_final_step = None
         self.rl_reward = 0.0
+        self.overwrite_weight = None
 
         # State Publisher
         self.state_pub = self.create_publisher(ActiveBOState, 'active_bo_state', 1)
@@ -112,6 +114,7 @@ class ActiveBOTopic(Node):
         self.bo_runs = 0
         self.bo_acq_fcn = None
         self.bo_metric_parameter = None
+        self.bo_metric_parameter_2 = None
         self.current_run = 0
         self.current_episode = 0
         self.save_result = False
@@ -172,6 +175,7 @@ class ActiveBOTopic(Node):
         if self.rl_pending:
             # self.get_logger().info('Active Reinforcement Learning response received!')
             self.rl_weights = np.array(msg.weights, dtype=np.float64)
+            self.overwrite_weight = np.array(msg.overwrite_weight, dtype=bool)
             self.rl_final_step = msg.final_step
             self.rl_reward = msg.reward
 
@@ -193,7 +197,7 @@ class ActiveBOTopic(Node):
                 self.user_asked = False
 
             self.rl_pending = False
-            self.reset_rl_response()
+            # self.reset_rl_response()
 
     def mainloop_callback(self):
         if not self.active_bo_pending:
@@ -223,7 +227,7 @@ class ActiveBOTopic(Node):
                 else:
                     seed = int(np.random.randint(1, 2147483647, 1)[0])
 
-                rl_msg = ActiveRL()
+                rl_msg = ActiveRLRequest()
                 rl_msg.env = self.bo_env
                 rl_msg.seed = seed
                 rl_msg.display_run = False
@@ -282,7 +286,7 @@ class ActiveBOTopic(Node):
 
                     np.savetxt(path, data, delimiter=',')
 
-                active_rl_request = ActiveRL()
+                active_rl_request = ActiveRLRequest()
 
                 if self.bo_fixed_seed:
                     seed = int(self.seed_array[0, best_policy_idx])
@@ -292,10 +296,10 @@ class ActiveBOTopic(Node):
 
                 active_rl_request.env = self.bo_env
                 active_rl_request.seed = seed
+                active_rl_request.display_run = True
+                active_rl_request.interactive_run = 1
                 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.interactive_run = 1
-                active_rl_request.display_run = True
 
                 self.active_rl_pub.publish(active_rl_request)
 
@@ -335,7 +339,7 @@ class ActiveBOTopic(Node):
                         if user_query.query():
                             self.last_query = self.current_episode
                             self.user_asked = True
-                            active_rl_request = ActiveRL()
+                            active_rl_request = ActiveRLRequest()
                             old_policy, y_max, old_weights, _ = self.BO.get_best_result()
 
                             # self.get_logger().info(f'Best: {y_max}, w:{old_weights}')
@@ -349,9 +353,9 @@ class ActiveBOTopic(Node):
                             active_rl_request.env = self.bo_env
                             active_rl_request.seed = seed
                             active_rl_request.display_run = True
+                            active_rl_request.interactive_run = 0
                             active_rl_request.policy = old_policy.tolist()
                             active_rl_request.weights = old_weights.tolist()
-                            active_rl_request.interactive_run = 0
 
                             # self.get_logger().info('Calling: Active RL')
                             self.active_rl_pub.publish(active_rl_request)
@@ -359,6 +363,16 @@ class ActiveBOTopic(Node):
 
                         else:
                             x_next = self.BO.next_observation()
+                            self.get_logger().info(f'x_next: {x_next}')
+                            self.get_logger().info(f'overwrite: {self.overwrite_weight}')
+                            self.get_logger().info(f'rl_weights: {self.rl_weights}')
+
+                            if self.overwrite:
+                                if self.overwrite_weight is not None and self.rl_weights is not None:
+                                    x_next[self.overwrite_weight] = self.rl_weights[self.overwrite_weight]
+                                    self.get_logger().info(f'x_next: {x_next}')
+                                    self.get_logger().info(f'overwrite: {self.overwrite_weight}')
+                                    self.get_logger().info(f'rl_weights: {self.rl_weights}')
                             # self.get_logger().info('Next Observation BO!')
                             self.BO.policy_model.weights = np.around(x_next, decimals=8)
                             if self.bo_fixed_seed:
@@ -366,13 +380,14 @@ class ActiveBOTopic(Node):
                             else:
                                 seed = int(np.random.randint(1, 2147483647, 1)[0])
 
-                            rl_msg = ActiveRL()
+                            rl_msg = ActiveRLRequest()
                             rl_msg.env = self.bo_env
                             rl_msg.seed = seed
                             rl_msg.display_run = False
                             rl_msg.interactive_run = 2
-                            rl_msg.weights = x_next.tolist()
                             rl_msg.policy = self.BO.policy_model.rollout().reshape(-1,).tolist()
+                            rl_msg.weights = x_next.tolist()
+
                             self.rl_pending = True
 
                             self.active_rl_pub.publish(rl_msg)
@@ -381,15 +396,12 @@ class ActiveBOTopic(Node):
                         self.reward[self.current_episode, self.current_run] = np.max(self.BO.Y)
                         self.get_logger().info(f'Current Episode: {self.current_episode},'
                                                f' best reward: {self.reward[self.current_episode, self.current_run]}')
+
                     else:
                         self.best_policy[:, self.current_run], \
                             self.best_pol_reward[:, self.current_run], \
                             self.best_weights[:, self.current_run], idx = self.BO.get_best_result()
 
-                        # self.get_logger().info(f'best idx: {idx}')
-
-                        # self.reward[:, self.current_run] = self.BO.best_reward.T
-
                         if self.current_run < self.bo_runs - 1:
                             self.BO = None