publisher doesnt work

.gitignore

@@ -3,3 +3,4 @@
 /build/
 /install/
 /log/
+/src/active_bo_ros/active_bo_ros/dump/

src/active_bo_msgs/CMakeLists.txt

@@ -25,8 +25,8 @@ rosidl_generate_interfaces(${PROJECT_NAME}
     "srv/BO.srv"
     "srv/ActiveBO.srv"
     "srv/ActiveRL.srv"
-    "srv/ActiveRLEval.srv"
     "msg/ImageFeedback.msg"
+    "msg/ActiveRLEval.msg"
 )
 
 if(BUILD_TESTING)

src/active_bo_msgs/msg/ActiveRLEval.msg

@@ -0,0 +1,2 @@
+float32[] policy
+float32[] weights

src/active_bo_ros/active_bo_ros/active_rl_service.py

@@ -1,10 +1,12 @@
 from active_bo_msgs.srv import ActiveRL
-from active_bo_msgs.srv import ActiveRLEval
 from active_bo_msgs.msg import ImageFeedback
+from active_bo_msgs.msg import ActiveRLEval
 
 import rclpy
 from rclpy.node import Node
 
+from rclpy.callback_groups import ReentrantCallbackGroup
+
 from active_bo_ros.ReinforcementLearning.ContinuousMountainCar import Continuous_MountainCarEnv
 
 import numpy as np
@@ -13,14 +15,32 @@ import numpy as np
 class ActiveRLService(Node):
     def __init__(self):
         super().__init__('active_rl_service')
-        self.srv = self.create_service(ActiveRL, 'active_rl_srv', self.active_rl_callback)
-        self.eval_srv = self.create_client(ActiveRLEval, 'active_rl_eval_srv')
+        srv_callback_group = ReentrantCallbackGroup()
+        sub_callback_group = ReentrantCallbackGroup()
+
+        self.srv = self.create_service(ActiveRL,
+                                       'active_rl_srv',
+                                       self.active_rl_callback,
+                                       callback_group=srv_callback_group)
 
         self.publisher = self.create_publisher(ImageFeedback, 'rl_feedback', 1)
 
+        self.eval_pub = self.create_publisher(ActiveRLEval, 'active_rl_eval_request', 1)
+        self.eval_sub = self.create_subscription(ActiveRLEval,
+                                                 'active_rl_eval_response',
+                                                 self.active_rl_eval_callback,
+                                                 10,
+                                                 callback_group=sub_callback_group)
+        self.eval_response_received = False
+        self.eval_response = None
+
         self.env = Continuous_MountainCarEnv(render_mode='rgb_array')
         self.distance_penalty = 0
 
+    def active_rl_eval_callback(self, response):
+        self.eval_response = response
+        self.eval_response_received = True
+
     def active_rl_callback(self, request, response):
 
         feedback_msg = ImageFeedback()
@@ -30,9 +50,9 @@ class ActiveRLService(Node):
         old_policy = request.old_policy
         old_weights = request.old_weights
 
-        eval_request = ActiveRLEval.Request()
-        eval_request.old_policy = old_policy
-        eval_request.old_weights = old_weights
+        eval_request = ActiveRLEval()
+        eval_request.policy = old_policy
+        eval_request.weights = old_weights
 
         self.env.reset()
 
@@ -63,9 +83,20 @@ class ActiveRLService(Node):
                 break
 
         self.get_logger().info('Enter new solution!')
-        eval_response = self.eval_srv.call(eval_request)
+        self.eval_pub.publish(eval_request)
 
-        new_policy = eval_response.new_policy.tolist()
+        while not self.eval_response_received:
+            rclpy.spin_once(self)
+
+        self.get_logger().info('Topic responded!')
+        new_policy = self.eval_response.policy
+        new_weights = self.eval_response.weights
+        self.eval_response_received = False
+        self.eval_response = None
+
+        reward = 0
+        step_count = 0
+        done = False
 
         for i in range(len(new_policy)):
             action = new_policy[i]
@@ -97,7 +128,8 @@ class ActiveRLService(Node):
                 distance = -(self.env.goal_position - output[0][0])
                 reward += distance * self.distance_penalty
 
-        response.new_weights = eval_response.Response.new_weights
+        self.get_logger().info(str(reward))
+        response.new_weights = new_weights
         response.reward = reward
         response.final_step = step_count
 

src/active_bo_ros/active_bo_ros/dump/active_rl_service_dump.py

@@ -0,0 +1,117 @@
+from active_bo_msgs.srv import ActiveRL
+from active_bo_msgs.srv import ActiveRLEval
+from active_bo_msgs.msg import ImageFeedback
+
+import rclpy
+from rclpy.node import Node
+
+from active_bo_ros.ReinforcementLearning.ContinuousMountainCar import Continuous_MountainCarEnv
+
+import numpy as np
+
+
+class ActiveRLService(Node):
+    def __init__(self):
+        super().__init__('active_rl_service')
+        self.srv = self.create_service(ActiveRL, 'active_rl_srv', self.active_rl_callback)
+        self.eval_srv = self.create_client(ActiveRLEval, 'active_rl_eval_srv')
+
+        self.publisher = self.create_publisher(ImageFeedback, 'rl_feedback', 1)
+
+        self.env = Continuous_MountainCarEnv(render_mode='rgb_array')
+        self.distance_penalty = 0
+
+    def active_rl_callback(self, request, response):
+
+        feedback_msg = ImageFeedback()
+
+        reward = 0
+        step_count = 0
+        old_policy = request.old_policy
+        old_weights = request.old_weights
+
+        eval_request = ActiveRLEval.Request()
+        eval_request.old_policy = old_policy
+        eval_request.old_weights = old_weights
+
+        self.env.reset()
+
+        self.get_logger().info('Best policy so far!')
+
+        for i in range(len(old_policy)):
+            action = old_policy[i]
+            output = self.env.step(action)
+
+            done = output[2]
+
+            rgb_array = self.env.render()
+            rgb_shape = rgb_array.shape
+
+            red = rgb_array[:, :, 0].flatten().tolist()
+            green = rgb_array[:, :, 1].flatten().tolist()
+            blue = rgb_array[:, :, 2].flatten().tolist()
+
+            feedback_msg.height = rgb_shape[0]
+            feedback_msg.width = rgb_shape[1]
+            feedback_msg.red = red
+            feedback_msg.green = green
+            feedback_msg.blue = blue
+
+            self.publisher.publish(feedback_msg)
+
+            if done:
+                break
+
+        self.get_logger().info('Enter new solution!')
+        eval_response = self.eval_srv.call(eval_request)
+        self.get_logger().info('Service responded!')
+
+        new_policy = eval_response.new_policy
+
+        for i in range(len(new_policy)):
+            action = new_policy[i]
+            output = self.env.step(action)
+
+            reward += output[1]
+            done = output[2]
+            step_count += 1
+
+            rgb_array = self.env.render()
+            rgb_shape = rgb_array.shape
+
+            red = rgb_array[:, :, 0].flatten().tolist()
+            green = rgb_array[:, :, 1].flatten().tolist()
+            blue = rgb_array[:, :, 2].flatten().tolist()
+
+            feedback_msg.height = rgb_shape[0]
+            feedback_msg.width = rgb_shape[1]
+            feedback_msg.red = red
+            feedback_msg.green = green
+            feedback_msg.blue = blue
+
+            self.publisher.publish(feedback_msg)
+
+            if done:
+                break
+
+            if not done and i == len(new_policy):
+                distance = -(self.env.goal_position - output[0][0])
+                reward += distance * self.distance_penalty
+
+        response.new_weights = eval_response.Response.new_weights
+        response.reward = reward
+        response.final_step = step_count
+
+        return response
+
+
+def main(args=None):
+    rclpy.init(args=args)
+
+    active_rl_service = ActiveRLService()
+
+    rclpy.spin(active_rl_service)
+
+
+if __name__ == '__main__':
+    main()

src/active_bo_ros/active_bo_ros/rl_service.py

@@ -8,6 +8,7 @@ from active_bo_ros.ReinforcementLearning.ContinuousMountainCar import Continuous
 
 import numpy as np
 
+
 class RLService(Node):
     def __init__(self):
         super().__init__('rl_service')
@@ -43,16 +44,6 @@ class RLService(Node):
             green = rgb_array[:, :, 1].flatten().tolist()
             blue = rgb_array[:, :, 2].flatten().tolist()
 
-            # red = [255] * 28800 + [0] * 28800 + [0] * 28800
-            # green = [0] * 28800 + [255] * 28800 + [0] * 28800
-            # blue = [0] * 28800 + [0] * 28800 + [255] * 28800
-
-
-            # random int data
-            # red = np.random.randint(0, 255, 240000).tolist()
-            # green = np.random.randint(0, 255, 240000).tolist()
-            # blue = np.random.randint(0, 255, 240000).tolist()
-
             feedback_msg.height = rgb_shape[0]
             feedback_msg.width = rgb_shape[1]
             feedback_msg.red = red