prepare for 2d bo

src/active_bo_msgs/msg/ActiveRLEvalResponse.msg

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

src/active_bo_msgs/msg/ActiveRLResponse.msg

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

src/active_bo_ros/active_bo_ros/BayesianOptimization/BO2D.py

@@ -0,0 +1,120 @@
+import numpy as np
+from sklearn.gaussian_process import GaussianProcessRegressor
+from sklearn.gaussian_process.kernels import Matern
+
+from active_bo_ros.PolicyModel.GaussianModelMultiDim import GaussianRBF
+
+from active_bo_ros.AcquisitionFunctions.ExpectedImprovement import ExpectedImprovement
+from active_bo_ros.AcquisitionFunctions.ProbabilityOfImprovement import ProbabilityOfImprovement
+from active_bo_ros.AcquisitionFunctions.ConfidenceBound import ConfidenceBound
+from active_bo_ros.AcquisitionFunctions.PreferenceExpectedImprovement import PreferenceExpectedImprovement
+
+from sklearn.exceptions import ConvergenceWarning
+import warnings
+
+warnings.filterwarnings('ignore', category=ConvergenceWarning)
+
+
+class BayesianOptimization:
+    def __init__(self, nr_steps, nr_dims, nr_weights, acq='ei', seed=None):
+        self.acq = acq
+        self.episode = 0
+
+        self.nr_steps = nr_steps
+        self.nr_dims = nr_dims
+        self.nr_weights = nr_weights
+        self.weights = self.nr_weights * self.nr_dims
+
+        self.lower_bound = -1.0
+        self.upper_bound = 1.0
+        self.seed = seed
+
+        self.X = None
+        self.Y = None
+        self.gp = None
+
+        self.policy_model = GaussianRBF(self.nr_steps, self.nr_weights, self.nr_dims,
+                                           lowerb=self.lower_bound, upperb=self.upper_bound, seed=seed)
+
+        self.acq_sample_size = 100
+
+        self.best_reward = np.empty((1, 1))
+
+        if acq == "Preference Expected Improvement":
+            self.acq_fun = PreferenceExpectedImprovement(self.weights,
+                                                         self.acq_sample_size,
+                                                         self.lower_bound,
+                                                         self.upper_bound,
+                                                         initial_variance=10.0,
+                                                         update_variance=0.05,
+                                                         seed=seed)
+
+        self.reset_bo()
+
+    def reset_bo(self):
+        self.gp = GaussianProcessRegressor(Matern(nu=1.5, ), n_restarts_optimizer=5) #length_scale=(1e-8, 1e5)
+        self.best_reward = np.empty((1, 1))
+        self.X = np.zeros((1, self.weights), dtype=np.float64)
+        self.Y = np.zeros((1, 1), dtype=np.float64)
+        self.episode = 0
+
+    def next_observation(self):
+        if self.acq == "Expected Improvement":
+            x_next = ExpectedImprovement(self.gp,
+                                         self.X,
+                                         self.acq_sample_size,
+                                         self.weights,
+                                         kappa=0,
+                                         seed=self.seed,
+                                         lower=self.lower_bound,
+                                         upper=self.upper_bound)
+
+        elif self.acq == "Probability of Improvement":
+            x_next = ProbabilityOfImprovement(self.gp,
+                                              self.X,
+                                              self.acq_sample_size,
+                                              self.weights,
+                                              kappa=0,
+                                              seed=self.seed,
+                                              lower=self.lower_bound,
+                                              upper=self.upper_bound)
+
+        elif self.acq == "Upper Confidence Bound":
+            x_next = ConfidenceBound(self.gp,
+                                     self.acq_sample_size,
+                                     self.weights,
+                                     beta=2.576,
+                                     seed=self.seed,
+                                     lower=self.lower_bound,
+                                     upper=self.upper_bound)
+
+        elif self.acq == "Preference Expected Improvement":
+            x_next = self.acq_fun.expected_improvement(self.gp,
+                                                       self.X,
+                                                       kappa=0)
+
+        else:
+            raise NotImplementedError
+
+        return x_next
+
+    def add_observation(self, reward, x):
+        if self.episode == 0:
+            self.X[0, :] = x
+            self.Y[0] = reward
+            self.best_reward[0] = np.max(self.Y)
+        else:
+            self.X = np.vstack((self.X, np.around(x, decimals=8)), dtype=np.float64)
+            self.Y = np.vstack((self.Y, reward), dtype=np.float64)
+            self.best_reward = np.vstack((self.best_reward, np.max(self.Y)), dtype=np.float64)
+
+        self.gp.fit(self.X, self.Y)
+        self.episode += 1
+
+    def get_best_result(self):
+        y_max = np.max(self.Y)
+        idx = np.argmax(self.Y)
+        x_max = self.X[idx, :]
+
+        return y_max, x_max, idx
+

src/active_bo_ros/active_bo_ros/PolicyModel/GaussianModelMultiDim.py

@@ -0,0 +1,49 @@
+import numpy as np
+
+
+class GaussianRBF:
+    def __init__(self, nr_steps, nr_weights, nr_dims, lowerb=-1.0, upperb=1.0, seed=None):
+        self.nr_weights = nr_weights
+        self.nr_steps = nr_steps
+        self.nr_dims = nr_dims
+
+        self.weights = None
+        self.trajectory = None
+
+        self.lowerb = lowerb
+        self.upperb = upperb
+
+        self.rng = np.random.default_rng(seed=seed)
+
+        # initialize
+        self.mid_points = np.linspace(0, self.nr_steps, self.nr_weights)
+        if nr_weights > 1:
+            self.std = self.mid_points[1] / (2 * np.sqrt(2 * np.log(2)))  # Full width at half maximum
+        else:
+            self.std = self.nr_steps / 2
+
+        self.reset()
+
+    def reset(self):
+        self.weights = np.zeros((self.nr_weights, self.nr_dims))
+        self.trajectory = np.zeros((self.nr_steps, self.nr_dims))
+
+    def random_weights(self):
+        for dim in range(self.nr_dims):
+            self.weights[:, dim] = self.rng.uniform(self.lowerb, self.upperb, self.nr_weights)
+
+    def rollout(self):
+        self.trajectory = np.zeros((self.nr_steps, self.nr_dims))
+        for step in range(self.nr_steps):
+            for weight in range(self.nr_weights):
+                base_fun = np.exp(-0.5 * (step - self.mid_points[weight]) ** 2 / self.std ** 2)
+                for dim in range(self.nr_dims):
+                    self.trajectory[step, dim] += base_fun * self.weights[weight, dim]
+
+        return self.trajectory
+
+    def set_weights(self, x):
+        self.weights = x.reshape(self.nr_weights, self.nr_dims)
+
+    def get_x(self):
+        return self.weights.reshape(self.nr_weights * self.nr_dims, 1)

src/active_bo_ros/active_bo_ros/interactive_bo.py

@@ -74,7 +74,7 @@ class ActiveBOTopic(Node):
         self.rl_weights = None
         self.rl_final_step = None
         self.rl_reward = 0.0
-        self.overwrite_weight = None
+        self.weight_preference = None
 
         # State Publisher
         self.state_pub = self.create_publisher(ActiveBOState, 'active_bo_state', 1)
@@ -175,7 +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.weight_preference = np.array(msg.weight_preference, dtype=bool)
             self.rl_final_step = msg.final_step
             self.rl_reward = msg.reward
 
@@ -195,7 +195,7 @@ class ActiveBOTopic(Node):
             if self.user_asked:
                 self.last_user_reward = self.rl_reward
                 if self.bo_acq_fcn == "Preference Expected Improvement":
-                    self.BO.acq_fun.update_proposal_model(self.rl_weights, self.overwrite_weight)
+                    self.BO.acq_fun.update_proposal_model(self.rl_weights, self.weight_preference)
                 self.user_asked = False
 
             self.rl_pending = False
@@ -371,14 +371,14 @@ class ActiveBOTopic(Node):
                             #     self.get_logger().info(f"X: {self.BO.X}")
                             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'overwrite: {self.weight_preference}')
                             # 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:
+                                if self.weight_preference is not None and self.rl_weights is not None:
-                                    x_next[self.overwrite_weight] = self.rl_weights[self.overwrite_weight]
+                                    x_next[self.weight_preference] = self.rl_weights[self.weight_preference]
                                     # self.get_logger().info(f'x_next: {x_next}')
-                                    # self.get_logger().info(f'overwrite: {self.overwrite_weight}')
+                                    # self.get_logger().info(f'overwrite: {self.weight_preference}')
                                     # 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)

src/active_bo_ros/active_bo_ros/interactive_bo_2d.py

@@ -0,0 +1,446 @@
+from active_bo_msgs.msg import ActiveBORequest
+from active_bo_msgs.msg import ActiveBOResponse
+from active_bo_msgs.msg import ActiveRLRequest
+from active_bo_msgs.msg import ActiveRLResponse
+from active_bo_msgs.msg import ActiveBOState
+
+import rclpy
+from rclpy.node import Node
+
+from rclpy.callback_groups import ReentrantCallbackGroup
+
+from active_bo_ros.BayesianOptimization.BO2D import BayesianOptimization
+
+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
+import os
+
+
+class ActiveBOTopic(Node):
+    def __init__(self):
+        super().__init__('active_bo_topic')
+
+        bo_callback_group = ReentrantCallbackGroup()
+        rl_callback_group = ReentrantCallbackGroup()
+        mainloop_callback_group = ReentrantCallbackGroup()
+
+        # Active Bayesian Optimization Publisher, Subscriber and Message attributes
+        self.active_bo_pub = self.create_publisher(ActiveBOResponse,
+                                                   'active_bo_response',
+                                                   1, callback_group=bo_callback_group)
+
+        self.active_bo_sub = self.create_subscription(ActiveBORequest,
+                                                      'active_bo_request',
+                                                      self.active_bo_callback,
+                                                      1, callback_group=bo_callback_group)
+
+        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_nr_dims = 2
+        self.bo_steps = 0
+        self.bo_episodes = 0
+        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
+        self.seed_array = None
+        self.save_result = False
+
+        # Active Reinforcement Learning Publisher, Subscriber and Message attributes
+        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,
+                                                      'active_rl_response',
+                                                      self.active_rl_callback,
+                                                      1, callback_group=rl_callback_group)
+
+        self.rl_pending = False
+        self.rl_weights = None
+        self.rl_final_step = None
+        self.rl_reward = 0.0
+        self.weight_preference = None
+
+        # State Publisher
+        self.state_pub = self.create_publisher(ActiveBOState, 'active_bo_state', 1)
+
+        # RL Environments and BO
+        self.env = None
+
+        self.BO = None
+        self.nr_init = 3
+        self.init_step = 0
+        self.init_pending = False
+        self.reward = None
+        self.best_reward = 0.0
+        self.best_pol_reward = None
+        self.best_policy = None
+        self.best_weights = None
+
+        # User Query
+        self.last_query = 0
+        self.user_asked = False
+        self.last_user_reward = 0.0
+        self.overwrite = False
+
+        # Main loop timer object
+        self.mainloop_timer_period = 0.1
+        self.mainloop = self.create_timer(self.mainloop_timer_period,
+                                          self.mainloop_callback,
+                                          callback_group=mainloop_callback_group)
+
+    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 = 0
+        self.bo_episodes = 0
+        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
+        self.seed_array = None
+        self.env = None
+        self.active_bo_pending = False
+        self.BO = None
+        self.overwrite = False
+
+    def active_bo_callback(self, msg):
+        if not self.active_bo_pending:
+            # 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
+            self.bo_episodes = msg.nr_episodes
+            self.bo_runs = msg.nr_runs
+            self.bo_acq_fcn = msg.acquisition_function
+            self.bo_metric_parameter = msg.metric_parameter
+            self.bo_metric_parameter_2 = msg.metric_parameter_2
+            self.save_result = msg.save_result
+            self.seed_array = np.zeros((1, self.bo_runs))
+            self.overwrite = msg.overwrite
+
+            # initialize
+            self.reward = np.zeros((self.bo_episodes + self.nr_init - 1, self.bo_runs))
+            self.best_pol_reward = np.zeros((1, self.bo_runs))
+            self.best_policy = np.zeros((self.bo_steps, self.bo_runs))
+            self.best_weights = np.zeros((self.bo_nr_weights, self.bo_runs))
+
+            # set the seed
+            if self.bo_fixed_seed:
+                self.seed = int(np.random.randint(1, 2147483647, 1)[0])
+                # self.get_logger().info(str(self.seed))
+            else:
+                self.seed = None
+
+            # set rl environment
+            if self.bo_env == "Reacher":
+                pass
+            else:
+                raise NotImplementedError
+
+    def reset_rl_response(self):
+        self.rl_weights = None
+        self.rl_final_step = None
+
+    def active_rl_callback(self, msg):
+        if self.rl_pending:
+            # self.get_logger().info('Active Reinforcement Learning response received!')
+            self.rl_weights = np.array(msg.weights, dtype=np.float64)
+            self.weight_preference = np.array(msg.weight_preference, dtype=bool)
+            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}')
+
+            if self.init_pending:
+                self.init_step += 1
+
+                if self.init_step == self.nr_init:
+                    self.init_step = 0
+                    self.init_pending = False
+
+            if self.user_asked:
+                self.last_user_reward = self.rl_reward
+                if self.bo_acq_fcn == "Preference Expected Improvement":
+                    self.BO.acq_fun.update_proposal_model(self.rl_weights, self.weight_preference)
+                self.user_asked = False
+
+            self.rl_pending = False
+            # self.reset_rl_response()
+
+    def mainloop_callback(self):
+        if not self.active_bo_pending:
+            return
+
+        else:
+            if self.rl_pending:
+                return
+
+            if self.BO is None:
+                self.BO = BayesianOptimization(self.bo_steps,
+                                               2,
+                                               self.bo_nr_weights,
+                                               acq=self.bo_acq_fcn)
+
+                self.BO.reset_bo()
+
+                # self.BO.initialize()
+                self.init_pending = True
+                self.get_logger().info('BO Initialization is starting!')
+                # self.get_logger().info(f'{self.rl_pending}')
+
+            if self.init_pending:
+                if self.bo_fixed_seed:
+                    seed = self.seed
+                else:
+                    seed = int(np.random.randint(1, 2147483647, 1)[0])
+
+                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 = self.BO.policy_model.random_policy().tolist()
+                rl_msg.policy = self.BO.policy_model.rollout().reshape(-1,).tolist()
+
+                self.active_rl_pub.publish(rl_msg)
+
+                self.rl_pending = True
+
+            if self.current_run == self.bo_runs:
+                bo_response = ActiveBOResponse()
+
+                best_policy_idx = np.argmax(self.best_pol_reward)
+                bo_response.best_policy = self.best_policy[:, best_policy_idx].tolist()
+                bo_response.best_weights = self.best_weights[:, best_policy_idx].tolist()
+
+                # self.get_logger().info(f'Best Policy: {self.best_pol_reward}')
+
+                self.get_logger().info(f'{best_policy_idx}, {int(self.seed_array[0, best_policy_idx])}')
+
+                bo_response.reward_mean = np.mean(self.reward, axis=1).tolist()
+                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'
+                    elif self.bo_acq_fcn == "Preference Expected Improvement":
+                        acq = 'pei'
+                    else:
+                        raise NotImplementedError
+
+                    home_dir = os.path.expanduser('~')
+                    file_path = os.path.join(home_dir, 'Documents/IntRLResults')
+                    filename = env + '-' + acq + '-' + self.bo_metric + '-' \
+                               + str(round(self.bo_metric_parameter, 2)) + '-' \
+                               + str(self.bo_nr_weights) + '-' + str(time.time())
+                    filename = filename.replace('.', '_') + '.csv'
+                    path = os.path.join(file_path, filename)
+
+                    data = self.reward
+
+                    np.savetxt(path, data, delimiter=',')
+
+                active_rl_request = ActiveRLRequest()
+
+                if self.bo_fixed_seed:
+                    seed = int(self.seed_array[0, best_policy_idx])
+                    # self.get_logger().info(f'Used seed{seed}')
+                else:
+                    seed = int(np.random.randint(1, 2147483647, 1)[0])
+
+                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()
+
+                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()
+
+            else:
+                if self.init_pending:
+                    return
+                else:
+                    if self.current_episode < self.bo_episodes + self.nr_init - 1:
+                        # metrics
+                        if self.bo_metric == "random":
+                            user_query = RandomQuery(self.bo_metric_parameter)
+
+                        elif self.bo_metric == "regular":
+                            user_query = RegularQuery(self.bo_metric_parameter, self.current_episode)
+
+                        elif self.bo_metric == "improvement":
+                            user_query = ImprovementQuery(self.bo_metric_parameter,
+                                                          self.bo_metric_parameter_2,
+                                                          self.last_query,
+                                                          self.reward[:self.current_episode, self.current_run])
+
+                        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)
+
+                        else:
+                            raise NotImplementedError
+
+                        if user_query.query():
+                            self.last_query = self.current_episode
+                            self.user_asked = True
+                            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}')
+                            # self.get_logger().info(f'Size of Y: {self.BO.Y.shape}, Size of X: {self.BO.X.shape}')
+
+                            if self.bo_fixed_seed:
+                                seed = self.seed
+                            else:
+                                seed = int(np.random.randint(1, 2147483647, 1)[0])
+
+                            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()
+
+                            # self.get_logger().info('Calling: Active RL')
+                            self.active_rl_pub.publish(active_rl_request)
+                            self.rl_pending = True
+
+                        else:
+                            # if self.bo_acq_fcn == "Preference Expected Improvement":
+                            #     self.get_logger().info(f"{self.BO.acq_fun.proposal_mean}")
+                            #     self.get_logger().info(f"X: {self.BO.X}")
+                            x_next = self.BO.next_observation()
+                            # self.get_logger().info(f'x_next: {x_next}')
+                            # self.get_logger().info(f'overwrite: {self.weight_preference}')
+                            # self.get_logger().info(f'rl_weights: {self.rl_weights}')
+
+                            if self.overwrite:
+                                if self.weight_preference is not None and self.rl_weights is not None:
+                                    x_next[self.weight_preference] = self.rl_weights[self.weight_preference]
+                                    # self.get_logger().info(f'x_next: {x_next}')
+                                    # self.get_logger().info(f'overwrite: {self.weight_preference}')
+                                    # 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:
+                                seed = self.seed
+                            else:
+                                seed = int(np.random.randint(1, 2147483647, 1)[0])
+
+                            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.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)
+
+                        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]}')
+                        self.current_episode += 1
+
+
+                    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()
+
+                        if self.current_run < self.bo_runs - 1:
+                            self.BO = None
+
+                        self.current_episode = 0
+                        self.last_query = 0
+                        if self.bo_fixed_seed:
+                            self.seed_array[0, self.current_run] = self.seed
+                            self.seed = int(np.random.randint(1, 2147483647, 1)[0])
+                            # self.get_logger().info(f'{self.seed}')
+                        self.current_run += 1
+                        self.get_logger().info(f'Current Run: {self.current_run}')
+
+            # send the current states
+
+            if self.BO is not None and self.BO.Y is not None:
+                self.best_reward = np.max(self.BO.Y)
+
+                state_msg = ActiveBOState()
+                state_msg.current_run = self.current_run + 1 if self.current_run < self.bo_runs else self.bo_runs
+                state_msg.current_episode = self.current_episode \
+                    if self.current_episode < self.bo_episodes else self.bo_episodes
+                state_msg.best_reward = float(self.best_reward)
+                state_msg.last_user_reward = self.last_user_reward
+                self.state_pub.publish(state_msg)
+
+
+def main(args=None):
+    rclpy.init(args=args)
+
+    active_bo_topic = ActiveBOTopic()
+
+    rclpy.spin(active_bo_topic)
+
+    try:
+        rclpy.spin(active_bo_topic)
+    except KeyboardInterrupt:
+        pass
+
+    active_bo_topic.destroy_node()
+    rclpy.shutdown()
+
+
+if __name__ == '__main__':
+    main()
+

src/active_bo_ros/active_bo_ros/interactive_rl_2d.py

@@ -0,0 +1,293 @@
+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
+
+import rclpy
+from rclpy.node import Node
+
+from rclpy.callback_groups import ReentrantCallbackGroup
+
+from active_bo_ros.ReinforcementLearning.ContinuousMountainCar import Continuous_MountainCarEnv
+from active_bo_ros.ReinforcementLearning.CartPole import CartPoleEnv
+from active_bo_ros.ReinforcementLearning.Pendulum import PendulumEnv
+from active_bo_ros.ReinforcementLearning.Acrobot import AcrobotEnv
+
+
+import numpy as np
+import time
+import copy
+
+
+class ActiveRL(Node):
+    def __init__(self):
+        super().__init__('active_rl_service')
+        rl_callback_group = ReentrantCallbackGroup()
+        topic_callback_group = ReentrantCallbackGroup()
+        mainloop_callback_group = ReentrantCallbackGroup()
+
+        # Active Reinforcement Learning Publisher, Subscriber and Message attributes
+        self.active_rl_pub = self.create_publisher(ActiveRLResponse,
+                                                   'active_rl_response',
+                                                   1, callback_group=rl_callback_group)
+        self.active_rl_sub = self.create_subscription(ActiveRLRequest,
+                                                      'active_rl_request',
+                                                      self.active_rl_callback,
+                                                      1, callback_group=rl_callback_group)
+
+        self.rl_env = None
+        self.rl_seed = None
+        self.rl_policy = None
+        self.rl_weights = None
+        self.rl_reward = 0.0
+        self.rl_step = 0
+
+        # Image publisher to publish the rgb array from the gym environment
+        self.image_pub = self.create_publisher(ImageFeedback,
+                                               'rl_feedback',
+                                               1, callback_group=topic_callback_group)
+
+        # Active RL Evaluation Publisher, Subscriber and Message attributes
+        self.eval_pub = self.create_publisher(ActiveRLEvalRequest,
+                                              'active_rl_eval_request',
+                                              1,
+                                              callback_group=topic_callback_group)
+        self.eval_sub = self.create_subscription(ActiveRLEvalResponse,
+                                                 'active_rl_eval_response',
+                                                 self.active_rl_eval_callback,
+                                                 1,
+                                                 callback_group=topic_callback_group)
+
+        self.eval_response_received = False
+        self.eval_policy = None
+        self.eval_weights = None
+        self.overwrite_weight = None
+
+        # RL Environments
+        self.env = None
+
+        # State Machine Variables
+        self.best_pol_shown = False
+        self.policy_sent = False
+        self.rl_pending = False
+        self.interactive_run = 0
+        self.display_run = False
+
+        # Main loop timer object
+        self.mainloop_timer_period = 0.1
+        self.mainloop = self.create_timer(self.mainloop_timer_period,
+                                          self.mainloop_callback,
+                                          callback_group=mainloop_callback_group)
+
+    def reset_rl_request(self):
+        self.rl_env = None
+        self.rl_seed = None
+        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
+        self.rl_seed = msg.seed
+        self.display_run = msg.display_run
+        self.rl_policy = np.array(msg.policy, dtype=np.float64)
+        self.rl_weights = msg.weights
+        self.interactive_run = msg.interactive_run
+
+        if self.rl_env == "Mountain Car":
+            self.env = Continuous_MountainCarEnv(render_mode="rgb_array")
+        elif self.rl_env == "Cartpole":
+            self.env = CartPoleEnv(render_mode="rgb_array")
+        elif self.rl_env == "Acrobot":
+            self.env = AcrobotEnv(render_mode="rgb_array")
+        elif self.rl_env == "Pendulum":
+            self.env = PendulumEnv(render_mode="rgb_array")
+        else:
+            raise NotImplementedError
+
+        # self.get_logger().info('Active RL: Called!')
+        self.env.reset(seed=self.rl_seed)
+        self.rl_pending = True
+        self.policy_sent = False
+        self.rl_step = 0
+
+    def reset_eval_request(self):
+        self.eval_policy = None
+        self.eval_weights = None
+
+    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)
+        self.eval_response_received = True
+
+    def next_image(self, policy, display_run):
+        action = policy[self.rl_step]
+        action_clipped = action.clip(min=-1.0, max=1.0)
+        output = self.env.step(action_clipped.astype(np.float64))
+
+        self.rl_reward += output[1]
+        done = output[2]
+        self.rl_step += 1
+
+        if display_run:
+            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 = ImageFeedback()
+
+            feedback_msg.height = rgb_shape[0]
+            feedback_msg.width = rgb_shape[1]
+            feedback_msg.current_time = self.rl_step
+            feedback_msg.red = red
+            feedback_msg.green = green
+            feedback_msg.blue = blue
+
+            self.image_pub.publish(feedback_msg)
+
+        if not done and self.rl_step == len(policy):
+            done = True
+
+        return done
+
+    def complete_run(self, policy):
+        env_reward = 0.0
+        step_count = 0
+
+        self.env.reset(seed=self.rl_seed)
+
+        for i in range(len(policy)):
+            action = policy[i]
+            action_clipped = action.clip(min=-1.0, max=1.0)
+            output = self.env.step(action_clipped.astype(np.float64))
+
+            env_reward += output[1]
+            done = output[2]
+            step_count += 1
+
+            if done:
+                break
+
+        self.env.reset(seed=self.rl_seed)
+        return env_reward, step_count
+
+    def mainloop_callback(self):
+        if self.rl_pending:
+            if self.interactive_run == 0:
+                if not self.best_pol_shown:
+                    if not self.policy_sent:
+                        self.rl_step = 0
+                        self.rl_reward = 0.0
+                        self.env.reset(seed=self.rl_seed)
+
+                        eval_request = ActiveRLEvalRequest()
+                        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, self.display_run)
+
+                    if done:
+                        self.best_pol_shown = True
+                        self.rl_step = 0
+                        self.rl_reward = 0.0
+
+                elif self.best_pol_shown:
+                    if not self.eval_response_received:
+                        pass
+
+                    if self.eval_response_received:
+                        done = self.next_image(self.eval_policy, self.display_run)
+
+                        if done:
+                            rl_response = ActiveRLResponse()
+                            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)
+
+                            self.env.reset(seed=self.rl_seed)
+
+                            # reset flags and attributes
+                            self.reset_eval_request()
+                            self.reset_rl_request()
+
+                            self.rl_step = 0
+                            self.rl_reward = 0.0
+
+                            self.best_pol_shown = False
+                            self.eval_response_received = False
+                            self.rl_pending = False
+
+            elif self.interactive_run == 1:
+                if not self.policy_sent:
+                    self.rl_step = 0
+                    self.rl_reward = 0.0
+                    self.env.reset(seed=self.rl_seed)
+
+                    eval_request = ActiveRLEvalRequest()
+                    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, self.display_run)
+
+                if done:
+                    self.rl_step = 0
+                    self.rl_reward = 0.0
+                    self.rl_pending = False
+
+            elif self.interactive_run == 2:
+                env_reward, step_count = self.complete_run(self.rl_policy)
+
+                rl_response = ActiveRLResponse()
+                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
+
+
+def main(args=None):
+    rclpy.init(args=args)
+
+    active_rl_service = ActiveRL()
+
+    rclpy.spin(active_rl_service)
+
+    rclpy.shutdown()
+
+
+if __name__ == '__main__':
+    main()