Path Planning of Robotic Fish in Unknown Environment with Improved Reinforcement Learning Algorithm

Author(s):  
Jingbo Hu ◽  
Jie Mei ◽  
Dingfang Chen ◽  
Lijie Li ◽  
Zhengshu Cheng
2021 ◽  
Vol 2138 (1) ◽  
pp. 012011
Author(s):  
Yanwei Zhao ◽  
Yinong Zhang ◽  
Shuying Wang

Abstract Path planning refers to that the mobile robot can obtain the surrounding environment information and its own state information through the sensor carried by itself, which can avoid obstacles and move towards the target point. Deep reinforcement learning consists of two parts: reinforcement learning and deep learning, mainly used to deal with perception and decision-making problems, has become an important research branch in the field of artificial intelligence. This paper first introduces the basic knowledge of deep learning and reinforcement learning. Then, the research status of deep reinforcement learning algorithm based on value function and strategy gradient in path planning is described, and the application research of deep reinforcement learning in computer game, video game and autonomous navigation is described. Finally, I made a brief summary and outlook on the algorithms and applications of deep reinforcement learning.


Symmetry ◽  
2022 ◽  
Vol 14 (1) ◽  
pp. 132
Author(s):  
Jianfeng Zheng ◽  
Shuren Mao ◽  
Zhenyu Wu ◽  
Pengcheng Kong ◽  
Hao Qiang

To solve the problems of poor exploration ability and convergence speed of traditional deep reinforcement learning in the navigation task of the patrol robot under indoor specified routes, an improved deep reinforcement learning algorithm based on Pan/Tilt/Zoom(PTZ) image information was proposed in this paper. The obtained symmetric image information and target position information are taken as the input of the network, the speed of the robot is taken as the output of the next action, and the circular route with boundary is taken as the test. The improved reward and punishment function is designed to improve the convergence speed of the algorithm and optimize the path so that the robot can plan a safer path while avoiding obstacles first. Compared with Deep Q Network(DQN) algorithm, the convergence speed after improvement is shortened by about 40%, and the loss function is more stable.


2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Xiaoyun Lei ◽  
Zhian Zhang ◽  
Peifang Dong

Dynamic path planning of unknown environment has always been a challenge for mobile robots. In this paper, we apply double Q-network (DDQN) deep reinforcement learning proposed by DeepMind in 2016 to dynamic path planning of unknown environment. The reward and punishment function and the training method are designed for the instability of the training stage and the sparsity of the environment state space. In different training stages, we dynamically adjust the starting position and target position. With the updating of neural network and the increase of greedy rule probability, the local space searched by agent is expanded. Pygame module in PYTHON is used to establish dynamic environments. Considering lidar signal and local target position as the inputs, convolutional neural networks (CNNs) are used to generalize the environmental state. Q-learning algorithm enhances the ability of the dynamic obstacle avoidance and local planning of the agents in environment. The results show that, after training in different dynamic environments and testing in a new environment, the agent is able to reach the local target position successfully in unknown dynamic environment.


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