moved the Evaluation from BayesianOptimization.py to the active_rl_topic.py due to inconsistency in the evaluation of rl envs
This commit is contained in:
parent
1b9e099696
commit
e99b131ee9
@ -1,5 +1,6 @@
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string env
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uint32 seed
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bool final_run
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bool display_run
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uint8 interactive_run
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float64[] policy
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float64[] weights
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@ -38,9 +38,11 @@ class BayesianOptimization:
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def reset_bo(self):
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self.counter_array = np.empty((1, 1))
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self.GP = None
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self.GP = GaussianProcessRegressor(Matern(nu=1.5, length_scale_bounds=(1e-8, 1e5)), n_restarts_optimizer=5, )
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self.episode = 0
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self.best_reward = np.empty((1, 1))
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self.X = np.zeros((1, self.nr_policy_weights), dtype=np.float64)
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self.Y = np.zeros((1, 1), dtype=np.float64)
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def runner(self, policy, seed=None):
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env_reward = 0.0
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@ -71,9 +73,6 @@ class BayesianOptimization:
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self.env.reset(seed=seed)
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self.reset_bo()
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self.X = np.zeros((self.nr_init, self.nr_policy_weights), dtype=np.float64)
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self.Y = np.zeros((self.nr_init, 1), dtype=np.float64)
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for i in range(self.nr_init):
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self.policy_model.random_policy()
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self.X[i, :] = self.policy_model.weights.T
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@ -141,16 +140,16 @@ class BayesianOptimization:
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return step_count
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def add_new_observation(self, reward, x_new):
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self.X = np.vstack((self.X, np.around(x_new, decimals=8)), dtype=np.float64)
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self.Y = np.vstack((self.Y, reward), dtype=np.float64)
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self.GP.fit(self.X, self.Y)
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if self.episode == 0:
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self.X[0, :] = x_new
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self.Y[0] = reward
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self.best_reward[0] = np.max(self.Y)
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else:
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self.X = np.vstack((self.X, np.around(x_new, decimals=8)), dtype=np.float64)
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self.Y = np.vstack((self.Y, reward), dtype=np.float64)
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self.best_reward = np.vstack((self.best_reward, np.max(self.Y)), dtype=np.float64)
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self.GP.fit(self.X, self.Y)
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self.episode += 1
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def get_best_result(self):
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@ -25,6 +25,7 @@ class GaussianRBF:
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def random_policy(self):
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self.weights = np.around(self.rng.uniform(self.low, self.upper, self.nr_weights), decimals=8)
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return self.weights.T
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def rollout(self):
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self.policy = np.zeros((self.nr_steps, 1))
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@ -68,8 +68,9 @@ class ActiveRLService(Node):
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# State Machine Variables
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self.best_pol_shown = False
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self.policy_sent = False
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self.active_rl_pending = False
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self.final_run = False
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self.rl_pending = False
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self.interactive_run = False
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self.display_run = False
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# Main loop timer object
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self.mainloop_timer_period = 0.05
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@ -86,9 +87,10 @@ class ActiveRLService(Node):
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def active_rl_callback(self, msg):
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self.rl_env = msg.env
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self.rl_seed = msg.seed
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self.display_run = msg.display_run
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self.rl_policy = np.array(msg.policy, dtype=np.float64)
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self.rl_weights = msg.weights
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self.final_run = msg.final_run
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self.interactive_run = msg.interactive_run
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if self.rl_env == "Mountain Car":
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self.env = Continuous_MountainCarEnv(render_mode="rgb_array")
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@ -103,7 +105,7 @@ class ActiveRLService(Node):
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self.get_logger().info('Active RL: Called!')
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self.env.reset(seed=self.rl_seed)
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self.active_rl_pending = True
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self.rl_pending = True
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self.policy_sent = False
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self.rl_step = 0
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@ -119,7 +121,7 @@ class ActiveRLService(Node):
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self.env.reset(seed=self.rl_seed)
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self.eval_response_received = True
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def next_image(self, policy):
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def next_image(self, policy, display_run):
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action = policy[self.rl_step]
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action_clipped = action.clip(min=-1.0, max=1.0)
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output = self.env.step(action_clipped.astype(np.float64))
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@ -128,6 +130,7 @@ class ActiveRLService(Node):
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done = output[2]
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self.rl_step += 1
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if display_run:
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rgb_array = self.env.render()
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rgb_shape = rgb_array.shape
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@ -152,8 +155,8 @@ class ActiveRLService(Node):
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return done
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def mainloop_callback(self):
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if self.active_rl_pending:
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if not self.final_run:
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if self.rl_pending:
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if self.interactive_run == 0:
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if not self.best_pol_shown:
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if not self.policy_sent:
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self.rl_step = 0
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@ -170,7 +173,7 @@ class ActiveRLService(Node):
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self.policy_sent = True
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done = self.next_image(self.rl_policy)
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done = self.next_image(self.rl_policy, self.display_run)
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if done:
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self.best_pol_shown = True
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@ -182,7 +185,7 @@ class ActiveRLService(Node):
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pass
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if self.eval_response_received:
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done = self.next_image(self.eval_policy)
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done = self.next_image(self.eval_policy, self.display_run)
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if done:
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rl_response = ActiveRLResponse()
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@ -203,8 +206,8 @@ class ActiveRLService(Node):
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self.best_pol_shown = False
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self.eval_response_received = False
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self.active_rl_pending = False
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else:
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self.rl_pending = False
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elif self.interactive_run == 1:
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if not self.policy_sent:
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self.rl_step = 0
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self.rl_reward = 0.0
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@ -220,13 +223,39 @@ class ActiveRLService(Node):
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self.policy_sent = True
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done = self.next_image(self.rl_policy)
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done = self.next_image(self.rl_policy, self.display_run)
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if done:
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self.rl_step = 0
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self.rl_reward = 0.0
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self.final_run = False
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self.active_rl_pending = False
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self.rl_pending = False
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elif self.interactive_run == 2:
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if not self.policy_sent:
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self.rl_step = 0
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self.rl_reward = 0.0
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self.env.reset(seed=self.rl_seed)
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self.policy_sent = True
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done = self.next_image(self.rl_policy, self.display_run)
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if done:
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rl_response = ActiveRLResponse()
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rl_response.weights = self.eval_weights
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rl_response.reward = self.rl_reward
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rl_response.final_step = self.rl_step
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self.active_rl_pub.publish(rl_response)
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# reset flags and attributes
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self.reset_eval_request()
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self.reset_rl_request()
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self.rl_step = 0
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self.rl_reward = 0.0
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self.rl_pending = False
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def main(args=None):
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399
src/active_bo_ros/active_bo_ros/interactive_bo.py
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399
src/active_bo_ros/active_bo_ros/interactive_bo.py
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@ -0,0 +1,399 @@
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from active_bo_msgs.msg import ActiveBORequest
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from active_bo_msgs.msg import ActiveBOResponse
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from active_bo_msgs.msg import ActiveRL
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from active_bo_msgs.msg import ActiveRLResponse
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from active_bo_msgs.msg import ActiveBOState
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import rclpy
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from rclpy.node import Node
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from rclpy.callback_groups import ReentrantCallbackGroup
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from active_bo_ros.BayesianOptimization.BayesianOptimization import BayesianOptimization
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from active_bo_ros.ReinforcementLearning.ContinuousMountainCar import Continuous_MountainCarEnv
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from active_bo_ros.ReinforcementLearning.CartPole import CartPoleEnv
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from active_bo_ros.ReinforcementLearning.Pendulum import PendulumEnv
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from active_bo_ros.ReinforcementLearning.Acrobot import AcrobotEnv
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from active_bo_ros.UserQuery.random_query import RandomQuery
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from active_bo_ros.UserQuery.regular_query import RegularQuery
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from active_bo_ros.UserQuery.improvement_query import ImprovementQuery
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from active_bo_ros.UserQuery.max_acq_query import MaxAcqQuery
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import numpy as np
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import time
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import os
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class ActiveBOTopic(Node):
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def __init__(self):
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super().__init__('active_bo_topic')
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bo_callback_group = ReentrantCallbackGroup()
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rl_callback_group = ReentrantCallbackGroup()
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mainloop_callback_group = ReentrantCallbackGroup()
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# Active Bayesian Optimization Publisher, Subscriber and Message attributes
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self.active_bo_pub = self.create_publisher(ActiveBOResponse,
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'active_bo_response',
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1, callback_group=bo_callback_group)
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self.active_bo_sub = self.create_subscription(ActiveBORequest,
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'active_bo_request',
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self.active_bo_callback,
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1, callback_group=bo_callback_group)
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self.active_bo_pending = False
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self.bo_env = None
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self.bo_metric = None
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self.bo_fixed_seed = False
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self.bo_nr_weights = None
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self.bo_steps = 0
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self.bo_episodes = 0
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self.bo_runs = 0
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self.bo_acq_fcn = None
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self.bo_metric_parameter = None
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self.current_run = 0
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self.current_episode = 0
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self.seed = None
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self.seed_array = None
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self.save_result = False
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# Active Reinforcement Learning Publisher, Subscriber and Message attributes
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self.active_rl_pub = self.create_publisher(ActiveRL,
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'active_rl_request',
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1, callback_group=rl_callback_group)
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self.active_rl_sub = self.create_subscription(ActiveRLResponse,
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'active_rl_response',
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self.active_rl_callback,
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1, callback_group=rl_callback_group)
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self.rl_pending = False
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self.rl_weights = None
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self.rl_final_step = None
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self.rl_reward = 0.0
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# State Publisher
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self.state_pub = self.create_publisher(ActiveBOState, 'active_bo_state', 1)
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# RL Environments and BO
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self.env = None
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self.BO = None
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self.nr_init = 3
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self.init_step = 0
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self.init_pending = False
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self.reward = None
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self.best_reward = 0.0
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self.best_pol_reward = None
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self.best_policy = None
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self.best_weights = None
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# Main loop timer object
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self.mainloop_timer_period = 0.1
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self.mainloop = self.create_timer(self.mainloop_timer_period,
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self.mainloop_callback,
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callback_group=mainloop_callback_group)
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def reset_bo_request(self):
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self.bo_env = None
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self.bo_metric = None
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self.bo_fixed_seed = False
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self.bo_nr_weights = None
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self.bo_steps = 0
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self.bo_episodes = 0
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self.bo_runs = 0
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self.bo_acq_fcn = None
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self.bo_metric_parameter = None
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self.current_run = 0
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self.current_episode = 0
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self.save_result = False
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self.seed_array = None
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def active_bo_callback(self, msg):
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if not self.active_bo_pending:
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self.get_logger().info('Active Bayesian Optimization request pending!')
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self.active_bo_pending = True
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self.bo_env = msg.env
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self.bo_metric = msg.metric
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self.bo_fixed_seed = msg.fixed_seed
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self.bo_nr_weights = msg.nr_weights
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self.bo_steps = msg.max_steps
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self.bo_episodes = msg.nr_episodes
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self.bo_runs = msg.nr_runs
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self.bo_acq_fcn = msg.acquisition_function
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self.bo_metric_parameter = msg.metric_parameter
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self.save_result = msg.save_result
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self.seed_array = np.zeros((1, self.bo_runs))
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# initialize
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self.reward = np.zeros((self.bo_episodes, self.bo_runs))
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self.best_pol_reward = np.zeros((1, self.bo_runs))
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self.best_policy = np.zeros((self.bo_steps, self.bo_runs))
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self.best_weights = np.zeros((self.bo_nr_weights, self.bo_runs))
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# set the seed
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if self.bo_fixed_seed:
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self.seed = int(np.random.randint(1, 2147483647, 1)[0])
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self.get_logger().info(str(self.seed))
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else:
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self.seed = None
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def reset_rl_response(self):
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self.rl_weights = None
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self.rl_final_step = None
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def active_rl_callback(self, msg):
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if self.rl_pending:
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self.get_logger().info('Active Reinforcement Learning response received!')
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self.rl_weights = msg.weights
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self.rl_final_step = msg.final_step
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self.rl_reward = msg.reward
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try:
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self.BO.add_new_observation(self.rl_reward, self.rl_weights)
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self.get_logger().info('Active Reinforcement Learning added new observation!')
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except Exception as e:
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self.get_logger().error(f'Active Reinforcement Learning failed to add new observation: {e}')
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if self.init_pending:
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self.init_step += 1
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if self.init_step == self.nr_init:
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self.init_step = 0
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self.init_pending = False
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self.rl_pending = False
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self.reset_rl_response()
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def mainloop_callback(self):
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if self.active_bo_pending:
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# set rl environment
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if self.bo_env == "Mountain Car":
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self.env = Continuous_MountainCarEnv()
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elif self.bo_env == "Cartpole":
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self.env = CartPoleEnv()
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elif self.bo_env == "Acrobot":
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self.env = AcrobotEnv()
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elif self.bo_env == "Pendulum":
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self.env = PendulumEnv()
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else:
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raise NotImplementedError
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if self.BO is None:
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self.BO = BayesianOptimization(self.env,
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self.bo_steps,
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nr_init=self.nr_init,
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acq=self.bo_acq_fcn,
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nr_weights=self.bo_nr_weights)
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# self.BO.initialize()
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self.init_pending = True
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if self.init_pending and not self.rl_pending:
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if self.bo_fixed_seed:
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seed = self.seed
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else:
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seed = int(np.random.randint(1, 2147483647, 1)[0])
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rl_msg = ActiveRL()
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rl_msg.env = self.bo_env
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rl_msg.seed = seed
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rl_msg.display_run = False
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rl_msg.interactive_run = 2
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rl_msg.weights = self.BO.policy_model.random_policy()
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rl_msg.policy = self.BO.policy_model.rollout()
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self.rl_pending = True
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if self.current_run == self.bo_runs:
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bo_response = ActiveBOResponse()
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best_policy_idx = np.argmax(self.best_pol_reward)
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bo_response.best_policy = self.best_policy[:, best_policy_idx].tolist()
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bo_response.best_weights = self.best_weights[:, best_policy_idx].tolist()
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self.get_logger().info(f'Best Policy: {self.best_pol_reward}')
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self.get_logger().info(f'{best_policy_idx}, {int(self.seed_array[0, best_policy_idx])}')
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bo_response.reward_mean = np.mean(self.reward, axis=1).tolist()
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bo_response.reward_std = np.std(self.reward, axis=1).tolist()
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if self.save_result:
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if self.bo_env == "Mountain Car":
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env = 'mc'
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elif self.bo_env == "Cartpole":
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env = 'cp'
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elif self.bo_env == "Acrobot":
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env = 'ab'
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elif self.bo_env == "Pendulum":
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env = 'pd'
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else:
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raise NotImplementedError
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if self.bo_acq_fcn == "Expected Improvement":
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acq = 'ei'
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elif self.bo_acq_fcn == "Probability of Improvement":
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acq = 'pi'
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elif self.bo_acq_fcn == "Upper Confidence Bound":
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acq = 'cb'
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else:
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raise NotImplementedError
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home_dir = os.path.expanduser('~')
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file_path = os.path.join(home_dir, 'Documents/IntRLResults')
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filename = env + '-' + acq + '-' + self.bo_metric + '-' \
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+ str(round(self.bo_metric_parameter, 2)) + '-' \
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+ str(self.bo_nr_weights) + '-' + str(time.time())
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filename = filename.replace('.', '_') + '.csv'
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path = os.path.join(file_path, filename)
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data = self.reward
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np.savetxt(path, data, delimiter=',')
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active_rl_request = ActiveRL()
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||||
|
||||
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.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
|
||||
|
||||
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()
|
||||
self.active_bo_pending = False
|
||||
self.BO = None
|
||||
|
||||
else:
|
||||
if self.rl_pending:
|
||||
pass
|
||||
else:
|
||||
if self.init_pending:
|
||||
pass
|
||||
elif self.current_episode < self.bo_episodes:
|
||||
# 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 == "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
|
||||
|
||||
if user_query.query():
|
||||
active_rl_request = ActiveRL()
|
||||
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.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)
|
||||
self.rl_pending = True
|
||||
|
||||
else:
|
||||
x_next = self.BO.next_observation()
|
||||
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 = ActiveRL()
|
||||
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
|
||||
rl_msg.policy = self.BO.policy_model.rollout()
|
||||
self.rl_pending = True
|
||||
|
||||
self.current_episode += 1
|
||||
# self.get_logger().info(f'Current Episode: {self.current_episode}')
|
||||
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
|
||||
|
||||
self.BO = None
|
||||
|
||||
self.current_episode = 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 + 1 \
|
||||
if self.current_episode < self.bo_episodes else self.bo_episodes
|
||||
state_msg.best_reward = float(self.best_reward)
|
||||
state_msg.last_user_reward = float(self.rl_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()
|
||||
Loading…
Reference in New Issue
Block a user