DQL size error

[johan606303]

Hi.

I am new with DQL. I am working with AirSim simulator, and I coded an algorithm on Python on Visual Studio, using keras, to teatch to the drone to avoid obstacles. When I launched the train, the algorithm looks like to work normaly in the begining, but after iteration 400, 1300 or 2308 (it always changes) I have the following error that appear.

I used 'reshape' function only in 2 functions :

and :

Or, here is the full code.

`
import numpy as np
import airsim
import time
import math
import tensorflow as tf
import keras
from airsim.utils import to_eularian_angles
from airsim.utils import to_quaternion
from keras.layers import Conv2D,Dense
from keras.layers import Activation
from keras.layers import MaxPool2D
from keras.layers import Dropout
from keras.layers import Input
import keras.backend as K
from keras.models import load_model
from keras import Input
from keras.layers import Flatten
from keras.activations import softmax,elu,relu
from keras.optimizers import Adam
from keras.optimizers import adam
from keras.models import Sequential
from keras.optimizers import Adam, RMSprop
from keras.models import Model
#tf.compat.v1.disable_eager_execution()
import random

from collections import deque

#tf.random_normal_initializer

client=airsim.MultirotorClient()
z=-5
memory_size=10000000
pos_0=client.getMultirotorState().kinematics_estimated.position

state_space=[84, 84]
action_size=3

def OurModel(state_size,action_space):

X_input=Input(state_size,name='Input')
X=Conv2D(filters=32,kernel_size=(8,8),strides=(2,2),padding='valid',activation='relu')(X_input)
X=MaxPool2D(pool_size=(2,2))(X)
X=Conv2D(filters=64,kernel_size=(5,5),strides=(3,3),padding='valid',activation='relu')(X)
X=MaxPool2D(pool_size=(2,2))(X)
X=Conv2D(filters=64,kernel_size=(2,2),strides=(1,1),padding='valid',activation='relu')(X)

X=Flatten()(X)

X=Dense(525,activation='relu')(X)
X=Dense(300,activation='relu')(X)
X_output=Dense(action_space,activation='softmax')(X)

model=Model(inputs = X_input, outputs = X_output)
#model.compile(loss=self.ourLoss(y_pred,y_real) , optimizer=Adam(lr=0.00025), metrics=["accuracy"])
model.compile(loss="mse", optimizer=RMSprop(lr=0.00025, rho=0.95, epsilon=0.01), metrics=["accuracy"])
model.summary()

return model

class MemoryClass():
def init(self,memory_size):
self.memory_size=memory_size
self.buffer=deque(maxlen=memory_size)
self.batch_size=64
#self.start_training=20

def add(self,experience):
    self.buffer.append(experience)


def sample(self):
    buffer_size=len(self.buffer)
    idx=np.random.choice(np.arange(buffer_size),self.batch_size,False)
    return [self.buffer[k] for k in idx]


def replay(self):
    batch=self.sample()
    next_states_mb=np.array([each[0] for each in batch],ndmin=3)
    actions_mb=np.array([each[1] for each in batch])
    states_mb=np.array([each[2] for each in batch],ndmin=3)
    rewards_mb=np.array([each[3] for each in batch])
    dones_mb=np.array([each[4] for each in batch])

    return next_states_mb, actions_mb, states_mb, rewards_mb,dones_mb

class Agent():
def init(self):
self.state_size=(84, 84,1)
self.action_space=3
#self.DQNNetwork=DQNN(state_size,action_space)
self.model1=OurModel(self.state_size,self.action_space)
self.memory_size=10000000
self.memory=MemoryClass(memory_size)
self.gamma=0.75
self.epsilon_min=0.001
self.epsilon=1.0
self.epsilon_decay=0.995
self.episodes=120
self.max_step=120
self.step=0
self.count=0
self.pos0=client.getMultirotorState().kinematics_estimated.position
self.z=-5
self.goal_pos=[50,50]
self.initial_position=[0,0]
self.initial_distance=np.sqrt((self.initial_position[0]-self.goal_pos[0])**2+(self.initial_position[1]-self.goal_pos[1])**2)
self.batch_size=30

def generate_state(self):
    responses = client.simGetImages([airsim.ImageRequest("0", airsim.ImageType.DepthPerspective, True, False)])
    img1d = np.array(responses[0].image_data_float, dtype=np.float)
    #img1d = 255/np.maximum(np.ones(img1d.size), img1d)
    img2d = np.reshape(img1d, (responses[0].height, responses[0].width))

    from PIL import Image
    image = Image.fromarray(img2d)
    im_final = np.array(image.resize((84, 84)).convert('L'))
    im_final=np.reshape(im_final,[*self.state_size])
    return im_final

def load(self, name):
    self.model1 = load_model(name)


def save(self, name):
    self.model1.save(name)

def get_yaw(self):
    quaternions=client.getMultirotorState().kinematics_estimated.orientation
    a,b,yaw_rad=to_eularian_angles(quaternions)
    yaw_deg=math.degrees(yaw_rad)
    return yaw_deg,yaw_rad

def rotate_left(self):
    client.moveByRollPitchYawrateZAsync(0,0,0.2,self.z,3)
    n=int(3*5)
    D=[]
    done=False
    for k in range(n):
        collision=client.simGetCollisionInfo().has_collided
        done=collision
        D.append(collision)
        time.sleep(3/(n*300))
    if True in D:
        done=True
    time.sleep(3/300)
    time.sleep(5/300)
    new_state=self.generate_state()
    return done,new_state

def rotate_right(self):
    client.moveByRollPitchYawrateZAsync(0,0,-0.2,self.z,3)
    n=int(3*5)
    D=[]
    done=False
    for k in range(n):
        collision=client.simGetCollisionInfo().has_collided
        done=collision
        D.append(collision)
        time.sleep(3/(n*300))
    if True in D:
        done=True
    time.sleep(3/300)
    time.sleep(5/300)
    new_state=self.generate_state()
    return done,new_state

def move_forward(self):
    yaw_deg,yaw_rad=self.get_yaw()
    #need rad
    vx=math.cos(yaw_rad)*0.25
    vy=math.sin(yaw_rad)*0.25
    client.moveByVelocityAsync(vx,vy,0,10,airsim.DrivetrainType.ForwardOnly,airsim.YawMode(False))
    done=False
    n=int(10*5)
    D=[]
    done=False
    for k in range(n):
        collision=client.simGetCollisionInfo().has_collided
        D.append(collision)
        time.sleep(3.4/(n*300))
    if True in D:
        done=True
    new_state=self.generate_state()
    time.sleep(15/300)
    return done,new_state

def step_function(self,action):
    # Returns action,new_state, done
    # Move forward 3 meters by Pitch
    done=False
    if action==0:
        done,new_state=self.move_forward()
    # Rotate to right by 20 degress
    elif action==1:
        done,new_state=self.rotate_right()
    # Rotate to left by 30 degress
    elif action==2:
        done,new_state=self.rotate_left()
    self.count+=1
    return action,new_state,done


def compute_reward(self,done):
    reward=0.0
    pos_now=client.getMultirotorState().kinematics_estimated.position
    dist=np.sqrt((pos_now.x_val-self.goal_pos[0])**2+(pos_now.y_val-self.goal_pos[1])**2)
    print('dist: ',dist)

    if done==False and self.step<self.max_step:
        reward+=(self.initial_distance-dist)*6
        if 10<self.step<40 and dist>self.initial_distance*3/4:
            reward=-2-(self.step-10)
        elif 50<self.step<80 and dist>self.initial_distance*2/4:
            reward=-36-(self.step-50)
        elif 80<self.step<self.max_step and dist>self.initial_distance*1/4:
            reward=-80-(self.step-80)
        elif dist<3:
            reward+=650.0
    elif done==True and dist>3:
        reward-=180.0
    print('reward: ',reward)
    return reward


def choose_action(self,state):
    r=np.random.rand()
    print('r: ',r)
    print('epsilon: ',self.epsilon)
    print()
    if r>self.epsilon and self.count>64:
        #print('predicted action')
        state=np.reshape(state,[1,*self.state_size])

        #action=np.argmax(self.DQNNetwork.OurModel.predict(state))
        action=np.argmax(self.model1.predict(state))
    else:
        action=random.randrange(self.action_space)
    return action


def reset(self):
    client.reset()


def initial_pos(self):
    client.enableApiControl(True)
    v=0.6
    #z0=client.getMultirotorState().kinematics_estimated.position.z_val
    #t=np.abs(z0-self.z)/v
    client.moveToZAsync(self.z,v).join()
    #time.sleep(t+1)
    

def epsilon_policy(self):
    # Update epsilon
    if self.epsilon>self.epsilon_min:
        self.epsilon*=self.epsilon_decay


def train(self):
    for episode in range(self.episodes):
        self.initial_pos()
        self.step=0
        state=self.generate_state()
        done=False
        total_reward,episode_rewards=[],[]
        while self.step<self.max_step:
            self.step+=1
            print('count:', self.count)
            choice=self.choose_action(state)
            self.epsilon_policy()
            action,next_state,done=self.step_function(choice)
            reward=self.compute_reward(done)
            episode_rewards.append(reward)
            if done==True:
                total_reward.append(sum(episode_rewards))
                self.memory.add([next_state,action,state,reward,done])
                self.step=self.max_step
                self.reset()
                print("episode: {}, epsilon: {:.5}, total reward :{}".format(episode, self.epsilon,total_reward[-1]))
                self.save("airsim-dqn.h5")
            else:
                state=next_state
                self.memory.add([next_state,action,state,reward,done])
            if len(self.memory.buffer)>64:
                next_states_mb, actions_mb, states_mb, rewards_mb,dones_mb=self.memory.replay()
                target = self.model1.predict(states_mb)
                target_next = self.model1.predict(next_states_mb)
                for k in range(len(dones_mb)):
                    if dones_mb[k]==True:
                        target[k][actions_mb[k]] = rewards_mb[k]
                    elif dones_mb[k]==False:
                        target[k][actions_mb[k]] = rewards_mb[k] + self.gamma * (np.amax(target_next[k]))
                self.model1.fit(x=states_mb,y=target,batch_size=self.batch_size)

agent=Agent()
agent.train()
`