turtlebot3_dqn相关的详细代码

一、运行turtlebot3_dqn主要程序

1、笔记本终端1

source activate py27
cd ~/catkin_ws
source devel/setup.bash
export TURTLEBOT3_MODEL=burger
roslaunch turtlebot3_gazebo turtlebot3_stage_1.launch

2、笔记本终端2

source activate py27
cd ~/catkin_ws
source devel/setup.bash
export TURTLEBOT3_MODEL=burger
roslaunch turtlebot3_dqn turtlebot3_dqn_stage_1.launch

二、终端1的turtlebot3_gazebo turtlebot3_stage_1.launch

1、turtlebot3_stage_1.launch

<launch>
  <arg name="model" default="$(env TURTLEBOT3_MODEL)" doc="model type [burger, waffle, waffle_pi]"/>
  <arg name="x_pos" default="0.0"/>
  <arg name="y_pos" default="0.0"/>
  <arg name="z_pos" default="0.0"/>

  <include file="$(find gazebo_ros)/launch/empty_world.launch">
    <arg name="world_name" value="$(find turtlebot3_gazebo)/worlds/turtlebot3_stage_1.world"/>
    <arg name="paused" value="false"/>
    <arg name="use_sim_time" value="0.1"/>
    <arg name="gui" value="true"/>
    <arg name="headless" value="false"/>
    <arg name="debug" value="false"/>
  </include>  


  <param name="robot_description" command="$(find xacro)/xacro.py $(find turtlebot3_description)/urdf/turtlebot3_$(arg model).urdf.xacro" />

  <node name="spawn_urdf" pkg="gazebo_ros" type="spawn_model" args="-urdf -model turtlebot3_burger -x $(arg x_pos) -y $(arg y_pos) -z $(arg z_pos) -param robot_description" />

</launch>

2、(find turtlebot3_gazebo)/worlds/turtlebot3_stage_1.world

<sdf version='1.6'>
  <world name='default'>
    <!-- A global light source -->
    <include>
      <uri>model://sun</uri>
    </include>

    <!-- A ground plane -->
    <include>
      <uri>model://ground_plane</uri>
    </include>

    <physics type="ode">
      <real_time_update_rate>1000.0</real_time_update_rate>
      <max_step_size>0.001</max_step_size>
      <real_time_factor>2</real_time_factor>
      <ode>
        <solver>
          <type>quick</type>
          <iters>150</iters>
          <precon_iters>0</precon_iters>
          <sor>1.400000</sor>
          <use_dynamic_moi_rescaling>1</use_dynamic_moi_rescaling>
        </solver>
        <constraints>
          <cfm>0.00001</cfm>
          <erp>0.2</erp>
          <contact_max_correcting_vel>2000.000000</contact_max_correcting_vel>
          <contact_surface_layer>0.01000</contact_surface_layer>
        </constraints>
      </ode>
    </physics>

    <!-- Load world -->
    <include>
      <uri>model://turtlebot3_square</uri>
    </include>

    <scene>
      <ambient>0.4 0.4 0.4 1</ambient>
      <background>0.7 0.7 0.7 1</background>
      <shadows>true</shadows>
    </scene>

    <gui fullscreen='0'>
      <camera name='user_camera'>
        <pose>0.0 0.0 17.0 0 1.5708 0</pose>
        <view_controller>orbit</view_controller>
      </camera>
    </gui>
  </world>
</sdf>

3、(find turtlebot3_description)/urdf/turtlebot3_$(arg model).urdf.xacro

turtlebot3_burger.urdf.xacro：

<?xml version="1.0" ?>
<robot name="turtlebot3_burger" xmlns:xacro="http://ros.org/wiki/xacro">
  <xacro:include filename="$(find turtlebot3_description)/urdf/common_properties.xacro"/>
  <xacro:include filename="$(find turtlebot3_description)/urdf/turtlebot3_burger.gazebo.xacro"/>

  <link name="base_footprint"/>

  <joint name="base_joint" type="fixed">
    <parent link="base_footprint"/>
    <child link="base_link"/>
    <origin xyz="0.0 0.0 0.010" rpy="0 0 0"/>
  </joint>

  <link name="base_link">
    <visual>
      <origin xyz="-0.032 0 0.0" rpy="0 0 0"/>
      <geometry>
        <mesh filename="package://turtlebot3_description/meshes/bases/burger_base.stl" scale="0.001 0.001 0.001"/>
      </geometry>
      <material name="light_black"/>
    </visual>

    <collision>
      <origin xyz="-0.032 0 0.070" rpy="0 0 0"/>
      <geometry>
        <box size="0.140 0.140 0.143"/>
      </geometry>
    </collision>

    <inertial>
      <origin xyz="0 0 0" rpy="0 0 0"/>
      <mass value="8.2573504e-01"/>
      <inertia ixx="2.2124416e-03" ixy="-1.2294101e-05" ixz="3.4938785e-05"
               iyy="2.1193702e-03" iyz="-5.0120904e-06"
               izz="2.0064271e-03" />
    </inertial>
  </link>

  <joint name="wheel_left_joint" type="continuous">
    <parent link="base_link"/>
    <child link="wheel_left_link"/>
    <origin xyz="0.0 0.08 0.023" rpy="-1.57 0 0"/>
    <axis xyz="0 0 1"/>
  </joint>

  <link name="wheel_left_link">
    <visual>
      <origin xyz="0 0 0" rpy="1.57 0 0"/>
      <geometry>
        <mesh filename="package://turtlebot3_description/meshes/wheels/left_tire.stl" scale="0.001 0.001 0.001"/>
      </geometry>
      <material name="dark"/>
    </visual>

    <collision>
      <origin xyz="0 0 0" rpy="0 0 0"/>
      <geometry>
        <cylinder length="0.018" radius="0.033"/>
      </geometry>
    </collision>

    <inertial>
      <origin xyz="0 0 0" />
      <mass value="2.8498940e-02" />
      <inertia ixx="1.1175580e-05" ixy="-4.2369783e-11" ixz="-5.9381719e-09"
               iyy="1.1192413e-05" iyz="-1.4400107e-11"
               izz="2.0712558e-05" />
      </inertial>
  </link>

  <joint name="wheel_right_joint" type="continuous">
    <parent link="base_link"/>
    <child link="wheel_right_link"/>
    <origin xyz="0.0 -0.080 0.023" rpy="-1.57 0 0"/>
    <axis xyz="0 0 1"/>
  </joint>

  <link name="wheel_right_link">
    <visual>
      <origin xyz="0 0 0" rpy="1.57 0 0"/>
      <geometry>
        <mesh filename="package://turtlebot3_description/meshes/wheels/right_tire.stl" scale="0.001 0.001 0.001"/>
      </geometry>
      <material name="dark"/>
    </visual>

    <collision>
      <origin xyz="0 0 0" rpy="0 0 0"/>
      <geometry>
        <cylinder length="0.018" radius="0.033"/>
      </geometry>
    </collision>

    <inertial>
      <origin xyz="0 0 0" />
      <mass value="2.8498940e-02" />
      <inertia ixx="1.1175580e-05" ixy="-4.2369783e-11" ixz="-5.9381719e-09"
               iyy="1.1192413e-05" iyz="-1.4400107e-11"
               izz="2.0712558e-05" />
      </inertial>
  </link>

  <joint name="caster_back_joint" type="fixed">
    <parent link="base_link"/>
    <child link="caster_back_link"/>
    <origin xyz="-0.081 0 -0.004" rpy="-1.57 0 0"/>
  </joint>

  <link name="caster_back_link">
    <collision>
      <origin xyz="0 0.001 0" rpy="0 0 0"/>
      <geometry>
        <box size="0.030 0.009 0.020"/>
      </geometry>
    </collision>

    <inertial>
      <origin xyz="0 0 0" />
      <mass value="0.005" />
      <inertia ixx="0.001" ixy="0.0" ixz="0.0"
               iyy="0.001" iyz="0.0"
               izz="0.001" />
    </inertial>
  </link>

  <joint name="imu_joint" type="fixed">
    <parent link="base_link"/>
    <child link="imu_link"/>
    <origin xyz="-0.032 0 0.068" rpy="0 0 0"/>
  </joint>

  <link name="imu_link"/>

  <joint name="scan_joint" type="fixed">
    <parent link="base_link"/>
    <child link="base_scan"/>
    <origin xyz="-0.032 0 0.172" rpy="0 0 0"/>
  </joint>

  <link name="base_scan">
    <visual>
      <origin xyz="0 0 0.0" rpy="0 0 0"/>
      <geometry>
        <mesh filename="package://turtlebot3_description/meshes/sensors/lds.stl" scale="0.001 0.001 0.001"/>
      </geometry>
      <material name="dark"/>
    </visual>

    <collision>
      <origin xyz="0.015 0 -0.0065" rpy="0 0 0"/>
      <geometry>
        <cylinder length="0.0315" radius="0.055"/>
      </geometry>
    </collision>

    <inertial>
      <mass value="0.114" />
      <origin xyz="0 0 0" />
      <inertia ixx="0.001" ixy="0.0" ixz="0.0"
               iyy="0.001" iyz="0.0"
               izz="0.001" />
    </inertial>
  </link>

</robot>

三、终端2的roslaunch turtlebot3_dqn turtlebot3_dqn_stage_1.launch

1、turtlebot3_dqn_stage_1.launch

turtlebot3_dqn_stage_1.launch：

<launch>
  <arg name="stage" default="1"/>
  <param name="stage_number"  value="$(arg stage)"/>
  <node pkg="turtlebot3_dqn" type="turtlebot3_dqn_stage_1" name="turtlebot3_dqn_stage_1" output="screen" />
</launch>

2、node pkg=“turtlebot3_dqn” type=“turtlebot3_dqn_stage_1”

turtlebot3_dqn_stage_1：

import rospy
import os
import json
import numpy as np
import random
import time
import sys
sys.path.append(os.path.dirname(os.path.abspath(os.path.dirname(__file__))))
from collections import deque
from std_msgs.msg import Float32MultiArray
from src.turtlebot3_dqn.environment_stage_1 import Env
from keras.models import Sequential, load_model
from keras.optimizers import RMSprop
from keras.layers import Dense, Dropout, Activation


EPISODES = 3000

class ReinforceAgent():
    def __init__(self, state_size, action_size):
        self.pub_result = rospy.Publisher('result', Float32MultiArray, queue_size=5)
        self.dirPath = os.path.dirname(os.path.realpath(__file__))
        self.dirPath = self.dirPath.replace('turtlebot3_dqn/nodes', 'turtlebot3_dqn/save_model/stage_1_')
        self.result = Float32MultiArray()

        self.load_model = True
        self.load_episode = 30
        self.state_size = state_size
        self.action_size = action_size
        self.episode_step = 6000
        self.target_update = 2000
        self.discount_factor = 0.99
        self.learning_rate = 0.00025
        self.epsilon = 1.0
        self.epsilon_decay = 0.99
        self.epsilon_min = 0.05
        self.batch_size = 64
        self.train_start = 64
        self.memory = deque(maxlen=1000000)

        self.model = self.buildModel()
        self.target_model = self.buildModel()

        self.updateTargetModel()

        if self.load_model:
            self.model.set_weights(load_model(self.dirPath+str(self.load_episode)+".h5").get_weights())

            with open(self.dirPath+str(self.load_episode)+'.json') as outfile:
                param = json.load(outfile)
                self.epsilon = param.get('epsilon')
		print param		# fyo
		print self.epsilon	# fyo


    def buildModel(self):
        model = Sequential()
        dropout = 0.2

        model.add(Dense(64, input_shape=(self.state_size,), activation='relu', kernel_initializer='lecun_uniform'))

        model.add(Dense(64, activation='relu', kernel_initializer='lecun_uniform'))
        model.add(Dropout(dropout))

        model.add(Dense(self.action_size, kernel_initializer='lecun_uniform'))
        model.add(Activation('linear'))
        model.compile(loss='mse', optimizer=RMSprop(lr=self.learning_rate, rho=0.9, epsilon=1e-06))
        model.summary()

        return model

    def getQvalue(self, reward, next_target, done):
        if done:
            return reward
        else:
            return reward + self.discount_factor * np.amax(next_target)

    def updateTargetModel(self):
        self.target_model.set_weights(self.model.get_weights())

    def getAction(self, state):
        if np.random.rand() <= self.epsilon:
            self.q_value = np.zeros(self.action_size)
            return random.randrange(self.action_size)
        else:
            q_value = self.model.predict(state.reshape(1, len(state)))
            self.q_value = q_value
            return np.argmax(q_value[0])


    def appendMemory(self, state, action, reward, next_state, done):
        self.memory.append((state, action, reward, next_state, done))

    def trainModel(self, target=False):
        mini_batch = random.sample(self.memory, self.batch_size)
        X_batch = np.empty((0, self.state_size), dtype=np.float64)
        Y_batch = np.empty((0, self.action_size), dtype=np.float64)

        for i in range(self.batch_size):
            states = mini_batch[i][0]
            actions = mini_batch[i][1]
            rewards = mini_batch[i][2]
            next_states = mini_batch[i][3]
            dones = mini_batch[i][4]

            q_value = self.model.predict(states.reshape(1, len(states)))
            self.q_value = q_value

            if target:
                next_target = self.target_model.predict(next_states.reshape(1, len(next_states)))

            else:
                next_target = self.model.predict(next_states.reshape(1, len(next_states)))

            next_q_value = self.getQvalue(rewards, next_target, dones)

            X_batch = np.append(X_batch, np.array([states.copy()]), axis=0)
            Y_sample = q_value.copy()

            Y_sample[0][actions] = next_q_value
            Y_batch = np.append(Y_batch, np.array([Y_sample[0]]), axis=0)

            if dones:
                X_batch = np.append(X_batch, np.array([next_states.copy()]), axis=0)
                Y_batch = np.append(Y_batch, np.array([[rewards] * self.action_size]), axis=0)

        self.model.fit(X_batch, Y_batch, batch_size=self.batch_size, epochs=1, verbose=0)

if __name__ == '__main__':
    rospy.init_node('turtlebot3_dqn_stage_1')
    pub_result = rospy.Publisher('result', Float32MultiArray, queue_size=5)
    pub_get_action = rospy.Publisher('get_action', Float32MultiArray, queue_size=5)
    result = Float32MultiArray()
    get_action = Float32MultiArray()

    state_size = 26
    action_size = 5

    env = Env(action_size)

    agent = ReinforceAgent(state_size, action_size)
    scores, episodes = [], []
    global_step = 0
    start_time = time.time()

    for e in range(agent.load_episode + 1, EPISODES):
        done = False
        state = env.reset()
        score = 0
        for t in range(agent.episode_step):
            action = agent.getAction(state)

            next_state, reward, done = env.step(action)

            agent.appendMemory(state, action, reward, next_state, done)

            if len(agent.memory) >= agent.train_start:
                if global_step <= agent.target_update:
                    agent.trainModel()
                else:
                    agent.trainModel(True)

            score += reward
            state = next_state
            get_action.data = [action, score, reward]
            pub_get_action.publish(get_action)

            if e % 10 == 0:
                agent.model.save(agent.dirPath + str(e) + '.h5')
                with open(agent.dirPath + str(e) + '.json', 'w') as outfile:
                    json.dump(param_dictionary, outfile)

            if t >= 500:
                rospy.loginfo("Time out!!")
                done = True

            if done:
                result.data = [score, np.max(agent.q_value)]
                pub_result.publish(result)
                agent.updateTargetModel()
                scores.append(score)
                episodes.append(e)
                m, s = divmod(int(time.time() - start_time), 60)
                h, m = divmod(m, 60)

                rospy.loginfo('Ep: %d score: %.2f memory: %d epsilon: %.2f time: %d:%02d:%02d',
                              e, score, len(agent.memory), agent.epsilon, h, m, s)
                param_keys = ['epsilon']
                param_values = [agent.epsilon]
                param_dictionary = dict(zip(param_keys, param_values))
                break

            global_step += 1
            if global_step % agent.target_update == 0:
                rospy.loginfo("UPDATE TARGET NETWORK")

        if agent.epsilon > agent.epsilon_min:
            agent.epsilon *= agent.epsilon_decay

3、from src.turtlebot3_dqn.environment_stage_1 import Env

environment_stage_1.py：

import rospy
import numpy as np
import math
from math import pi
from geometry_msgs.msg import Twist, Point, Pose
from sensor_msgs.msg import LaserScan
from nav_msgs.msg import Odometry
from std_srvs.srv import Empty
from tf.transformations import euler_from_quaternion, quaternion_from_euler
from respawnGoal import Respawn

class Env():
    def __init__(self, action_size):
        self.goal_x = 0
        self.goal_y = 0
        self.heading = 0
        self.action_size = action_size
        self.initGoal = True
        self.get_goalbox = False
        self.position = Pose()
        self.pub_cmd_vel = rospy.Publisher('cmd_vel', Twist, queue_size=5)
        self.sub_odom = rospy.Subscriber('odom', Odometry, self.getOdometry)
        self.reset_proxy = rospy.ServiceProxy('gazebo/reset_simulation', Empty)
        self.unpause_proxy = rospy.ServiceProxy('gazebo/unpause_physics', Empty)
        self.pause_proxy = rospy.ServiceProxy('gazebo/pause_physics', Empty)
        self.respawn_goal = Respawn()

    def getGoalDistace(self):
        goal_distance = round(math.hypot(self.goal_x - self.position.x, self.goal_y - self.position.y), 2)

        return goal_distance

    def getOdometry(self, odom):
        self.position = odom.pose.pose.position
        orientation = odom.pose.pose.orientation
        orientation_list = [orientation.x, orientation.y, orientation.z, orientation.w]
        _, _, yaw = euler_from_quaternion(orientation_list)

        goal_angle = math.atan2(self.goal_y - self.position.y, self.goal_x - self.position.x)

        heading = goal_angle - yaw
        if heading > pi:
            heading -= 2 * pi

        elif heading < -pi:
            heading += 2 * pi

        self.heading = round(heading, 2)

    def getState(self, scan):
        scan_range = []
        heading = self.heading
        min_range = 0.13
        done = False

        for i in range(len(scan.ranges)):
            if scan.ranges[i] == float('Inf'):
                scan_range.append(3.5)
            elif np.isnan(scan.ranges[i]):
                scan_range.append(0)
            else:
                scan_range.append(scan.ranges[i])

        if min_range > min(scan_range) > 0:
            done = True

        current_distance = round(math.hypot(self.goal_x - self.position.x, self.goal_y - self.position.y),2)
        if current_distance < 0.2:
            self.get_goalbox = True

        return scan_range + [heading, current_distance], done

    def setReward(self, state, done, action):
        yaw_reward = []
        current_distance = state[-1]
        heading = state[-2]

        for i in range(5):
            angle = -pi / 4 + heading + (pi / 8 * i) + pi / 2
            tr = 1 - 4 * math.fabs(0.5 - math.modf(0.25 + 0.5 * angle % (2 * math.pi) / math.pi)[0])
            yaw_reward.append(tr)

        distance_rate = 2 ** (current_distance / self.goal_distance)
        reward = ((round(yaw_reward[action] * 5, 2)) * distance_rate)

        if done:
            rospy.loginfo("Collision!!")
            reward = -200
            self.pub_cmd_vel.publish(Twist())

        if self.get_goalbox:
            rospy.loginfo("Goal!!")
            reward = 200
            self.pub_cmd_vel.publish(Twist())
            self.goal_x, self.goal_y = self.respawn_goal.getPosition(True, delete=True)
            self.goal_distance = self.getGoalDistace()
            self.get_goalbox = False

        return reward

    def step(self, action):
        max_angular_vel = 1.5
        ang_vel = ((self.action_size - 1)/2 - action) * max_angular_vel * 0.5

        vel_cmd = Twist()
        vel_cmd.linear.x = 0.15
        vel_cmd.angular.z = ang_vel
        self.pub_cmd_vel.publish(vel_cmd)
	print vel_cmd # fyo

        data = None
        while data is None:
            try:
                data = rospy.wait_for_message('scan', LaserScan, timeout=5)
            except:
                pass

        state, done = self.getState(data)
        reward = self.setReward(state, done, action)

        return np.asarray(state), reward, done

    def reset(self):
        rospy.wait_for_service('gazebo/reset_simulation')
        try:
            self.reset_proxy()
        except (rospy.ServiceException) as e:
            print("gazebo/reset_simulation service call failed")

        data = None
        while data is None:
            try:
                data = rospy.wait_for_message('scan', LaserScan, timeout=5)
            except:
                pass

        if self.initGoal:
            self.goal_x, self.goal_y = self.respawn_goal.getPosition()
            self.initGoal = False

        self.goal_distance = self.getGoalDistace()
        state, done = self.getState(data)

        return np.asarray(state)