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changed back to gaussian basis from dmp

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Niko Feith 2023-09-15 10:51:17 +02:00
parent 1dbad3fa0d
commit 54c196715e
1 changed files with 22 additions and 35 deletions
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57
src/active_bo_ros/active_bo_ros/interactive_bo_robot.py
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@ -4,7 +4,6 @@ from active_bo_msgs.msg import ActiveBOResponse
from active_bo_msgs.msg import ActiveRLRequest from active_bo_msgs.msg import ActiveRLRequest
from active_bo_msgs.msg import ActiveRLResponse from active_bo_msgs.msg import ActiveRLResponse
from active_bo_msgs.msg import ActiveBOState from active_bo_msgs.msg import ActiveBOState
from active_bo_msgs.msg import DMP
import rclpy import rclpy
from rclpy.node import Node from rclpy.node import Node
@ -60,7 +59,7 @@ class ActiveBOTopic(Node):
self.save_result = False self.save_result = False
# Active Reinforcement Learning Publisher, Subscriber and Message attributes # Active Reinforcement Learning Publisher, Subscriber and Message attributes
self.active_rl_pub = self.create_publisher(DMP, self.active_rl_pub = self.create_publisher(ActiveRLRequest,
'active_rl_request', 'active_rl_request',
1, callback_group=rl_callback_group) 1, callback_group=rl_callback_group)
self.active_rl_sub = self.create_subscription(ActiveRLResponse, self.active_rl_sub = self.create_subscription(ActiveRLResponse,
@ -143,7 +142,7 @@ class ActiveBOTopic(Node):
self.overwrite = msg.overwrite self.overwrite = msg.overwrite
# initialize # initialize
self.reward = np.ones((self.bo_runs, self.bo_episodes + self.nr_init - 1)) * -self.bo_steps self.reward = np.ones((self.bo_runs, self.bo_episodes + self.nr_init - 1)) * -200
self.best_pol_reward = np.ones((self.bo_runs, 1)) * -self.bo_steps self.best_pol_reward = np.ones((self.bo_runs, 1)) * -self.bo_steps
self.best_policy = np.zeros((self.bo_runs, self.bo_steps, self.bo_nr_dims)) self.best_policy = np.zeros((self.bo_runs, self.bo_steps, self.bo_nr_dims))
self.best_weights = np.zeros((self.bo_runs, self.bo_nr_weights, self.bo_nr_dims)) self.best_weights = np.zeros((self.bo_runs, self.bo_nr_weights, self.bo_nr_dims))
@ -204,25 +203,18 @@ class ActiveBOTopic(Node):
self.BO.reset_bo() self.BO.reset_bo()
# self.BO.initialize()
self.init_pending = True self.init_pending = True
self.get_logger().info('BO Initialization is starting!') self.get_logger().info('BO Initialization is starting!')
# self.get_logger().info(f'{self.rl_pending}')
if self.init_pending: if self.init_pending:
if self.bo_fixed_seed:
seed = self.seed
else:
seed = int(np.random.randint(1, 2147483647, 1)[0])
w = self.BO.policy_model.random_weights() rl_msg = ActiveRLRequest()
rl_msg = DMP()
rl_msg.interactive_run = 2 rl_msg.interactive_run = 2
rl_msg.p_x = w[:, 0] rl_msg.weights = self.BO.policy_model.random_weights().flatten('F').tolist()
rl_msg.p_y = w[:, 1] rl_msg.policy = self.BO.policy_model.rollout().flatten('F').tolist()
rl_msg.nr_weights = self.bo_nr_weights
rl_msg.nr_steps = self.bo_steps rl_msg.nr_steps = self.bo_steps
rl_msg.nr_bfs = self.bo_nr_weights rl_msg.nr_dims = self.bo_nr_dims
self.active_rl_pub.publish(rl_msg) self.active_rl_pub.publish(rl_msg)
@ -271,16 +263,13 @@ class ActiveBOTopic(Node):
np.savetxt(path, data, delimiter=',') np.savetxt(path, data, delimiter=',')
rl_msg = ActiveRLRequest()
w = self.best_weights[best_policy_idx, :, :]
rl_msg = DMP()
rl_msg.interactive_run = 1 rl_msg.interactive_run = 1
rl_msg.p_x = w[:, 0] rl_msg.weights = self.BO.policy_model.random_weights().flatten('F').tolist()
rl_msg.p_y = w[:, 1] rl_msg.policy = self.BO.policy_model.rollout().flatten('F').tolist()
rl_msg.nr_weights = self.bo_nr_weights
rl_msg.nr_steps = self.bo_steps rl_msg.nr_steps = self.bo_steps
rl_msg.nr_bfs = self.bo_nr_weights rl_msg.nr_dims = self.bo_nr_dims
self.active_rl_pub.publish(rl_msg) self.active_rl_pub.publish(rl_msg)
@ -328,14 +317,13 @@ class ActiveBOTopic(Node):
self.BO.policy_model.set_weights(old_weights) self.BO.policy_model.set_weights(old_weights)
w = self.BO.policy_model.weights rl_msg = ActiveRLRequest()
rl_msg = DMP()
rl_msg.interactive_run = 0 rl_msg.interactive_run = 0
rl_msg.p_x = w[:, 0] rl_msg.weights = self.BO.policy_model.random_weights().flatten('F').tolist()
rl_msg.p_y = w[:, 1] rl_msg.policy = self.BO.policy_model.rollout().flatten('F').tolist()
rl_msg.nr_weights = self.bo_nr_weights
rl_msg.nr_steps = self.bo_steps rl_msg.nr_steps = self.bo_steps
rl_msg.nr_bfs = self.bo_nr_weights rl_msg.nr_dims = self.bo_nr_dims
self.active_rl_pub.publish(rl_msg) self.active_rl_pub.publish(rl_msg)
@ -348,14 +336,13 @@ class ActiveBOTopic(Node):
x_next = self.BO.next_observation() x_next = self.BO.next_observation()
self.BO.policy_model.set_weights(np.around(x_next, decimals=8)) self.BO.policy_model.set_weights(np.around(x_next, decimals=8))
w = self.BO.policy_model.weights rl_msg = ActiveRLRequest()
rl_msg = DMP()
rl_msg.interactive_run = 1 rl_msg.interactive_run = 1
rl_msg.p_x = w[:, 0] rl_msg.weights = self.BO.policy_model.random_weights().flatten('F').tolist()
rl_msg.p_y = w[:, 1] rl_msg.policy = self.BO.policy_model.rollout().flatten('F').tolist()
rl_msg.nr_weights = self.bo_nr_weights
rl_msg.nr_steps = self.bo_steps rl_msg.nr_steps = self.bo_steps
rl_msg.nr_bfs = self.bo_nr_weights rl_msg.nr_dims = self.bo_nr_dims
self.active_rl_pub.publish(rl_msg) self.active_rl_pub.publish(rl_msg)