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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
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
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):
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
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
agent=Agent()
agent.train()
`
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