Application of AI based reinforcement learning to robot vehicle control (original) (raw)
ICCSA 2024, 2024
Currently, autonomous cars are extensively studied by many institutions and companies with the aim of practical implementation. Q-learning is an algorithm of reinforcement learning, which does not require a model and can be seen as an asynchronous method of dynamic programming. It allows agents to learn to take action optimally in a Markovian environment by experiencing the outcomes of actions without the need to build a probability model. This paper presents the process of developing an autonomous vehicle model for educational purposes that can self-optimize its decision-making options based on a reinforcement learning algorithm such as Q-learning. The algorithm helps train autonomous cars to avoid obstacles collisions. The preliminary results show that the Q-learning algorithm is successful in building a self-training technique to adapt to specific requirements.
Conference on Developments in Computer Science Budapest, Hungary, June 17-19, 2021, 2021
With the continuous improvement of global economic integration, the pace of global urbanization and modernization has been accelerated. And with the agricultural and industrial revolutions, the urban population began to expand at an unprecedented rate. Today, 55% of the world's population lives in urban areas, a proportion that is expected to increase to 68% by 2050, additional 2.5 billion city dwellers worldwide. Along with the physical expansion of cities, there are many problems, such as water management problems, social order problems, traffic problems. Among them like the blood of the city traffic, more and more become an obstacle to urban development. Solving the problem of urban traffic congestion and optimize the efficiency of urban traffic is an essential link in building a smart city. Intelligent transportation is an important part of a smart city. How to scientifically design and build intelligent transportation is a very valuable problem. Reinforcement learning, as an important part of the Computer Science, has been widely used in various social fields such as Health, transportation, education, finance, and so on. This paper will discuss the application of intelligent transportation control systems through the investigation and research of intelligent transportation combined with reinforcement learning.
This paper presents a self-improving reactive control system for autonomous robotic navigation. The navigation module uses a schemabased reactive control system to perform the navigation task. The learning module combines case-based reasoning and reinforcement learning to continuously tune the navigation system through experience. The case-based reasoning component perceives and characterizes the system's environment, retrieves an appropriate case, and uses the recommendations of the case to tune the parameters of the reactive control system. The reinforcement learning component refines the content of the cases based on the current experience. Together, the learning components perform on-line adaptation, resulting in improved performance as the reactive control system tunes itself to the environment, as well as on-line learning, resulting in an improved library of cases that capture environmental regularities necessary to perform on-line adaptation. The system is extensively evaluated through simulation studies using several performance metrics and system configurations.
A Method of Deep Reinforcement Learning for Simulation of Autonomous Vehicle Control
2021
Nowadays autonomous driving is expected to revolutionize the transportation sector. Carmakers, researchers, and administrators have been working on this field for years and significant progress has been made. However, the doubts and challenges to overcome are still huge, regarding not only complex technologies but also human awareness, culture, current traffic infrastructure. In terms of technical perspective, the accurate detection of obstacles, avoiding adjacent obstacles, and automatic navigation through the environment are some of the difficult problems. In this paper, an approach for solving those problems is proposed by using of Policy Gradient to control a simulated car via reinforcement learning. The proposed method is worked effectively to train an agent to control the simulated car in Unity ML-agents Highway, which is a simulating environment. This environment is chosen from some criteria of an environment simulating autonomous vehicle. The testing of the proposed method got positive results. Beside the average speed was well, the agent successfully learned the turning operation, progressively gaining the ability to navigate larger sections of the simulated raceway without crashing.
Reinforcement learning for the control of traffic flow on highways
2018
By submitting this dissertation electronically, I declare that the entirety of the work contained therein is my own, original work, that I am the sole author thereof (save to the extent explicitly otherwise stated), that reproduction and publication thereof by Stellenbosch University will not infringe any third party rights and that I have not previously in its entirety or in part submitted it for obtaining any qualification.
Control Navigation in Robots Using Reinforcement Learning
International Research Journal of Computer Science
Reinforcement learning (RL) is a subfield of machine learning which is being developed in Artificial Intelligence (AI). This technique is a data independent process. The primary aim of systems this kind is to maximize their reward signal which makes systems do better things trending to goal. Reinforcement Learning alters with techniques like supervised and unsupervised in such a way that in RL the agent gets up with its own insights and maps what action to perform in certain situations. On the other hand, Supervised and unsupervised have answers already embedded in them. In RL, in absence of new data, it can learn from its own experience where others can do. RL is used almost everywhere, the best applications of RL in Robotics specifically in motion control, planning it is also used in finance, gaming etc. Here is this paper demonstrating the navigation and motion control development of a 2 wheeled differential drive robot with the help of reinforcement learning topology. Traditionally, to design the behaviour of controllers in robots, we inevitably need models of how the robot actually behaves in the environment. But here we come up with a RL approach to design the control structure for the robot to navigate in the indoor environment.
Constraint reinforcement learning in autonomous driving
2020
Reinforcement learning (RL) is an increasingly important technology for developing highly-capable AI systems. The goal is for RL agents to explore their environments in order to learn optimal behaviors. Essentially, they operate on the principle of trial and error: they try out different actions and increase the likelihood of good behaviors and decrease the likelihood of bad behaviors. RL can effectively be used in autonomous driving where safety is a critical concern and errors are unacceptable. This can be achieved by placing constraints for formulating safety requirement, meaning that dangerous behaviour is minimized. In this paper we propose a new autonomous driving environment for the agent to learn appropriate behaviour in traffic. The agent is trained using two different algorithms Trust Region Policy Optimization and Proximal Policy Optimization algorithms.
Reinforcement learning in autonomous multi-vehicle systems: A structured review
2024
Unmanned autonomous vehicles become increasingly important in various application domains, such as manufacturing, logistics, communication, and the military. They are typically designed to navigate a specific environment, such as land, air, or water. However, many of the corresponding tasks require multiple vehicles to collaborate in a coordinated manner. Since these vehicles operate in dynamic environments, algorithms that are based on Reinforcement Learning (RL) are particularly well suited to achieve a high level of coordination. In this paper, we review the scientific literature that applies RL to control and coordinate multi-vehicle systems. A classification scheme is developed to analyze relevant articles with regard to various aspects of the application context and the technical implementations of the RL algorithms. Based on the results, we delineate the current state of research, identify current trends, and propose future research avenues in this field. • Computing methodologies → Reinforcement learning; Multiagent systems; Mobile agents.
Reinforcement Learning for Position Control Problem of a Mobile Robot
IEEE Access, 2020
Due to the increase in complexity in autonomous vehicles, most of the existing control systems are proving to be inadequate. Reinforcement Learning is gaining traction as it is posed to overcome these difficulties in a natural way. This approach allows an agent that interacts with the environment to get rewards for appropriate actions, learning to improve its performance continuously. The article describes the design and development of an algorithm to control the position of a wheeled mobile robot using Reinforcement Learning. One main advantage of this approach concerning traditional control algorithms is that the learning process is carried out automatically with a recursive procedure forward in time. Moreover, given the fidelity of the model for the particular implementation described in this work, the whole learning process can be carried out in simulation. This fact avoids damages to the actual robot during the learning stage. It shows that the position control of the robot (or...
Reinforcement Learning based Control of Imitative Policies for Near-Accident Driving
Robotics: Science and Systems XVI
Autonomous driving has achieved significant progress in recent years, but autonomous cars are still unable to tackle high-risk situations where a potential accident is likely. In such near-accident scenarios, even a minor change in the vehicle's actions may result in drastically different consequences. To avoid unsafe actions in near-accident scenarios, we need to fully explore the environment. However, reinforcement learning (RL) and imitation learning (IL), two widely-used policy learning methods, cannot model rapid phase transitions and are not scalable to fully cover all the states. To address driving in near-accident scenarios, we propose a hierarchical reinforcement and imitation learning (H-REIL) approach that consists of low-level policies learned by IL for discrete driving modes, and a high-level policy learned by RL that switches between different driving modes. Our approach exploits the advantages of both IL and RL by integrating them into a unified learning framework. Experimental results and user studies suggest our approach can achieve higher efficiency and safety compared to other methods. Analyses of the policies demonstrate our high-level policy appropriately switches between different low-level policies in near-accident driving situations.