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() `