def __init__(self):
# specify the dimension of observation space and action space
self.observation_space = 44
self.action_space = 4 # joint efforts
# We assume that a ROS node has already been created before initialising the environment
self.running_step = rospy.get_param("/running_step")
self.weight_r1 = rospy.get_param("/weight_r1")
self.weight_r2 = rospy.get_param("/weight_r2")
self.weight_r3 = rospy.get_param("/weight_r3")
# self.inr_r3 = rospy.get_param("/inr_r3")
# stablishes connection with simulator
self.gazebo = GazeboConnection()
self.mrm_state_object = MrmState(weight_r1=self.weight_r1,
weight_r2=self.weight_r2,
weight_r3=self.weight_r3)
self.mrm_joint_pubisher_object = JointPub()
self._seed()
self.reset_controller_pub = rospy.Publisher('/reset_controller',
Bool,
queue_size=1)
self.pause_sim_pub = rospy.Publisher('/pause_sim', Bool, queue_size=1)
# list for recording the minimal value from the laser
self.mini_laser = []
self.moco = model_control()
self.pipe_angle = 0.0
def __init__(self):
# specify the dimension of observation space and action space
self.observation_space = 22
self.action_space = 4
# We assume that a ROS node has already been created before initialising the environment
# gets training parameters from param server
self.desired_target = Point()
self.running_step = rospy.get_param("/running_step")
self.done_reward = rospy.get_param("/collision_reward")
self.weight_r1 = rospy.get_param("/weight_r1")
self.weight_r2 = rospy.get_param("/weight_r2")
self.weight_r3 = rospy.get_param("/weight_r3")
# stablishes connection with simulator
self.gazebo = GazeboConnection()
self.controllers_object = ControllersConnection(namespace="")
self.mrm_state_object = MrmState(weight_r1=self.weight_r1,
weight_r2=self.weight_r2,
weight_r3=self.weight_r3)
self.mrm_joint_pubisher_object = JointPub()
self._seed()
self.reset_controller_pub = rospy.Publisher('/reset_controller',
Bool,
queue_size=1)
self.pause_sim_pub = rospy.Publisher('/pause_sim', Bool, queue_size=1)
self.moco = model_control()
self.pipe_angle = 0.0
# these parameters should stay the same
"task": "Mrm-v0",
"n_steps": 2400,
"gamma": 0.98,
"lamda":
0.975, # Best lambda value is lower than gamma, empirically lambda introduces far less bias than gamma for a reasonably accruate value function'
"vf_constraint": 0.01,
"max_kl": .01,
"coef_en": 0.01, # coefficient for entropy bonus, only used in PPO
"lr_ph": 0.0006, # learning rate for adam, only used in PPO
"cg_damping": 0.001,
"max_pathlength": 90,
"n_validation": 120
}
moco = model_control()
# Create the Gym environment
env = gym.make('Mrm-v0')
env = env.unwrapped
rospy.loginfo("Gym environment done")
# position and orientation of the end-effector
global end_pos
def end_effector_pos_callback(msg):
global end_pos
end_pos = msg
# Get the position of end-effector
rospy.Subscriber("/end_effector_pos", Transform, end_effector_pos_callback)