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added random, regular, max acquisition & improvement metric

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This commit is contained in:
Niko Feith 2023-05-30 15:49:49 +02:00
parent 952ee10b67
commit c5f00a4069
2 changed files with 28 additions and 11 deletions
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4
src/active_bo_ros/active_bo_ros/BayesianOptimization/BayesianOptimization.py
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@ -86,7 +86,7 @@ class BayesianOptimization:
self.Y[i] = reward
self.GP = GaussianProcessRegressor(Matern(nu=1.5))
self.GP = GaussianProcessRegressor(Matern(nu=1.5, length_scale_bounds=(1e-8, 1e5)), n_restarts_optimizer=5, )
self.GP.fit(self.X, self.Y)
def next_observation(self):
@ -147,6 +147,8 @@ class BayesianOptimization:
self.X = np.vstack((self.X, x_new))
self.Y = np.vstack((self.Y, reward))
self.GP.fit(self.X, self.Y)
if self.episode == 0:
self.best_reward[0] = max(self.Y)
else:

35
src/active_bo_ros/active_bo_ros/active_bo_topic.py
View File

@ -132,12 +132,20 @@ class ActiveBOTopic(Node):
def active_rl_callback(self, msg):
if self.active_rl_pending:
self.get_logger().info('Active Reinforcement Learning response pending!')
self.active_rl_pending = False
self.get_logger().info('Active Reinforcement Learning response received!')
self.rl_weights = msg.weights
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}')
self.active_rl_pending = False
self.reset_rl_response()
def mainloop_callback(self):
if self.active_bo_pending:
@ -181,18 +189,26 @@ class ActiveBOTopic(Node):
else:
if self.active_rl_pending:
pass
elif self.rl_weights is not None and not self.active_rl_pending:
try:
self.BO.add_new_observation(self.rl_reward, self.rl_weights)
self.reset_rl_response()
except Exception as e:
self.get_logger().error(f'Active Reinforcement Learning failed to add new observation: {e}')
else:
if self.current_episode < self.bo_episodes:
# metrics
if self.bo_metric == "RandomQuery":
if self.bo_metric == "random":
user_query = RandomQuery(self.bo_metric_parameter)
elif self.bo_metric == "regular":
user_query = RegularQuery(self.bo_metric_parameter)
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)
elif self.bo_metric == "improvement":
user_query = ImprovementQuery(self.bo_metric_parameter, 10)
else:
raise NotImplementedError
@ -234,7 +250,6 @@ class ActiveBOTopic(Node):
self.get_logger().info(f'Current Run: {self.current_run}')
def main(args=None):
rclpy.init(args=args)