An end-to-end reinforcement learning method for automated guided vehicle path planning

2020 ◽  
Author(s):  
Yu Sun ◽  
Haisheng Li
Keyword(s):  
Reinforcement Learning ◽  
Path Planning ◽  
Automated Guided Vehicle ◽  
Learning Method ◽  
End To End ◽  
Vehicle Path

Autonomous underwater vehicle path planning based on actor-multi-critic reinforcement learning

2020 ◽  
pp. 095965182093708
Author(s):  
Zhuo Wang ◽  
Shiwei Zhang ◽  
Xiaoning Feng ◽  
Yancheng Sui
Keyword(s):  
Reinforcement Learning ◽  
Path Planning ◽  
Value Function ◽  
Autonomous Underwater Vehicle ◽  
Autonomous Underwater Vehicles ◽  
Underwater Vehicle ◽  
Learning Efficiency ◽  
Environmental Adaptability ◽  
Vehicle Path ◽  
The Value Function

The environmental adaptability of autonomous underwater vehicles is always a problem for its path planning. Although reinforcement learning can improve the environmental adaptability, the slow convergence of reinforcement learning is caused by multi-behavior coupling, so it is difficult for autonomous underwater vehicle to avoid moving obstacles. This article proposes a multi-behavior critic reinforcement learning algorithm applied to autonomous underwater vehicle path planning to overcome problems associated with oscillating amplitudes and low learning efficiency in the early stages of training which are common in traditional actor–critic algorithms. Behavior critic reinforcement learning assesses the actions of the actor from perspectives such as energy saving and security, combining these aspects into a whole evaluation of the actor. In this article, the policy gradient method is selected as the actor part, and the value function method is selected as the critic part. The strategy gradient and the value function methods for actor and critic, respectively, are approximated by a backpropagation neural network, the parameters of which are updated using the gradient descent method. The simulation results show that the method has the ability of optimizing learning in the environment and can improve learning efficiency, which meets the needs of real time and adaptability for autonomous underwater vehicle dynamic obstacle avoidance.

scholarly journals Application of Deep Reinforcement Learning Algorithm in Uncertain Logistics Transportation Scheduling

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Yunmei Yuan ◽  
Hongyu Li ◽  
Lili Ji
Keyword(s):  
Reinforcement Learning ◽  
Path Planning ◽  
New Technologies ◽  
Learning Algorithm ◽  
Computing Time ◽  
Optimal Solution ◽  
Optimization Strategy ◽  
Logistics Industry ◽  
Vehicle Path ◽  
The Impact

Nowadays, finding the optimal route for vehicles through online vehicle path planning is one of the main problems that the logistics industry needs to solve. Due to the uncertainty of the transportation system, especially the last-mile delivery problem of small packages in uncertain logistics transportation, the calculation of logistics vehicle routing planning becomes more complex than before. Most of the existing solutions are less applied to new technologies such as machine learning, and most of them use a heuristic algorithm. This kind of solution not only needs to set a lot of constraints but also requires much calculation time in the logistics network with high demand density. To design the uncertain logistics transportation path with minimum time, this paper proposes a new optimization strategy based on deep reinforcement learning that converts the uncertain online logistics routing problems into vehicle path planning problems and designs an embedded pointer network for obtaining the optimal solution. Considering the long time to solve the neural network, it is unrealistic to train parameters through supervised data. This article uses an unsupervised method to train the parameters. Because the process of parameter training is offline, this strategy can avoid the high delay. Through the simulation part, it is not difficult to see that the strategy proposed in this paper will effectively solve the uncertain logistics scheduling problem under the limited computing time, and it is significantly better than other strategies. Compared with traditional mathematical procedures, the algorithm proposed in this paper can reduce the driving distance by 60.71%. In addition, this paper also studies the impact of some key parameters on the effect of the program.

Research on Path-Planning of Manipulator Based on Multi-Agent Reinforcement Learning

2010 ◽  
Vol 44-47 ◽  
pp. 2116-2120
Author(s):  
Liang Tong
Keyword(s):  
Reinforcement Learning ◽  
Path Planning ◽  
Dynamic Characteristic ◽  
Simulation Experiment ◽  
Robot Manipulator ◽  
Variable Structure ◽  
Learning Method ◽  
New Approach ◽  
Multi Agent ◽  
Kohonen Net

Because of the dynamic characteristic of high nonlinear,strong coupling and variable structure,it is difficult to perform effective controlling on the robot manipulator by conventional controlling theory.In this paper,a new approach of multi-agent reinforcement learning method based on Kohonen net is proposed which is used in the multi-agent environment of robot manipulator path-planning and the simulation experiment shows the validity of this method.

scholarly journals Trajectory Simulation Approach for Autonomous Vehicles Path Planning using Deep Reinforcement Learning

2020 ◽  
Vol 8 (12) ◽  
pp. 436-454
Author(s):  
Jean Phelipe De Oliveira Lima ◽  
Raimundo Correa de Oliveira ◽  
Cleinaldo de Almeida Costa
Keyword(s):  
Reinforcement Learning ◽  
Path Planning ◽  
Prior Knowledge ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Convergence Time ◽  
Trajectory Simulation ◽  
Art Works ◽  
Vehicle Path ◽  
Two Phases

Autonomous vehicle path planning aims to allow safe and rapid movement in an environment without human interference. Recently, Reinforcement Learning methods have been used to solve this problem and have achieved satisfactory results. This work presents the use of Deep Reinforcement Learning for the task of path planning for autonomous vehicles through trajectory simulation, to define routes that offer greater safety (without collisions) and less distance for the displacement between two points. A method for creating simulation environments was developed to analyze the performance of the proposed models in different difficult degrees of circumstances. The decision-making strategy implemented was based on the use of Artificial Neural Networks of the Multilayer Perceptron type with parameters and hyperparameters determined from a grid search. The models were evaluated for their reward charts resulting from their learning process. Such evaluation occurred in two phases: isolated evaluation, in which the models were inserted into the environment without prior knowledge; and incremental evaluation, in which models were inserted in unknown environments with previous intelligence accumulated in other conditions. The results obtained are competitive with state-of-the-art works and highlight the adaptive characteristic of the models presented, which, when inserted with prior knowledge in environments, can reduce the convergence time by up to 89.47% when compared to related works.

scholarly journals TRAINING NEURAL NETWORKS USING REINFORCEMENT LEARNING TO REACTIVE PATH PLANNING

2020 ◽  
pp. 1-9
Author(s):  
Milton Calderón ◽  
Esperanza Camargo Casallas
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Reinforcement Learning ◽  
Path Planning ◽  
Mobile Robots ◽  
Research Group ◽  
Network Architectures ◽  
Learning Method ◽  
Two Dimensional ◽  
Final Point

The mobile robots are devices with great boom given the possibilities that their utilities offer, and to a greater extent, those freelancers who do not require an operator to perform their functions. In order to consolidate the autonomy it is necessary to generate a system of planning of ways that allows a viable route and as far as possible optimal. This study develops a reactive two-dimensional path planning method with neural networks trained under the reinforcement learning method. The complexity of the scenario between the initial and final point is due to warning and forbidden obstacle zones, and the experimentation is carried out on different neural network architectures, each one as an agent of the learning-by-reinforcement algorithm, being these DQN and DDQN types. The best results are obtained with the DDQN training, reaching the objective in 89% in the validation episodes, although the DQN method shows to be 15.63% faster in its success cases. This work was carried out within the research group DIGITI of the Universidad Distrital Francisco José de Caldas.

A Plant Control Technology Using Reinforcement Learning Method with Automatic Reward Adjustment

2009 ◽  
Vol 129 (7) ◽  
pp. 1253-1263
Author(s):  
Toru Eguchi ◽  
Takaaki Sekiai ◽  
Akihiro Yamada ◽  
Satoru Shimizu ◽  
Masayuki Fukai
Keyword(s):  
Reinforcement Learning ◽  
Control Technology ◽  
Learning Method ◽  
Plant Control
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