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Journal of Intelligent and Connected Vehicles 2024年第3期7卷 229-244页

作者： Riccardo Berta Luca Lazzaroni Alessio Capello Marianna Cossu Luca Forneris Alessandro Pighetti Francesco Bellotti Electrical Electronics and Telecommunication Engineering and Naval Architecture Department(DITEN)University of GenoaGenoa 16145Italy

This study provides a systematic analysis of the resource-consuming training of deep reinforcement-learning (DRL) agents for simulated low-speed automated driving (AD). In Unity, this study established two case studies: garage parking and navigating an obstacle-dense area. Our analysis involves training a path-planning agent with real-time-only sensor information. This study addresses research questions insufficiently covered in the literature, exploring curriculum learning (CL), agent generalization (knowledge transfer), computation distribution (CPU vs. GPU), and mapless navigation. CL proved necessary for the garage scenario and beneficial for obstacle avoidance. It involved adjustments at different stages, including terminal conditions, environment complexity, and reward function hyperparameters, guided by their evolution in multiple training attempts. Fine-tuning the simulation tick and decision period parameters was crucial for effective training. The abstraction of high-level concepts (e.g., obstacle avoidance) necessitates training the agent in sufficiently complex environments in terms of the number of obstacles. While blogs and forums discuss training machine learning models in Unity, a lack of scientific articles on DRL agents for AD persists. However, since agent development requires considerable training time and difficult procedures, there is a growing need to support such research through scientific means. In addition to our findings, we contribute to the R&D community by providing our environment with open sources.

关键词： automated driving autonomous agents deep reinforcement learning curriculum learning modeling and simulation

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agents learned,but do we?Knowledge discovery using the agent-based double auction markets

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Frontiers of Electrical and Electronic Engineering in China 2011年第1期6卷 159-170页

作者： Shu-Heng CHEN Tina YU Department of Economics Chengchi UniversityTaipeiChina Department of Computer Science Memorial University of NewfoundlandCanada

This paper demonstrates the potential role of autonomous agents in economic *** first dispatch autonomous agents,built by genetic programming,to double auction *** then study the bargaining strategies,discovered by them,and from there,an autonomous-agent-inspired economic theory with regard to the optimal procrastination is derived.

关键词： agent-based double auction markets autonomous agents genetic programming bargaining strategies monopsony procrastination strategy

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DISOPE distributed model predictive control of cascade systems with network communication

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控制理论与应用（英文版） 2005年第2期3卷 131-138页

作者： Yan ZHANG Shaoyuan LI Institute of Automation Shanghai Jiao Tong University Shanghai 200030 China

A novel distributed model predictive control scheme based on dynamic integrated system optimization and parameter estimation （DISOPE） was proposed for nonlinear cascade systems under network environment. Under the distributed control structure, online optimization of the cascade system was composed of several cascaded agents that can cooperate and exchange information via network communication. By iterating on modified distributed linear optimal control problems on the basis of estimating parameters at every iteration the correct optimal control action of the nonlinear model predictive control problem of the cascade system could be obtained, assuming that the algorithm was convergent. This approach avoids solving the complex nonlinear optimization problem and significantly reduces the computational burden. The simulation results of the fossil fuel power unit are illustrated to verify the effectiveness and practicability of the proposed algorithm.

关键词： Cascade systems Dynamic integrated system optimization and parameter estimation (DISOPE) Model predictive control (MPC) Distributed control system (DCS) autonomous agents Fossil fuel power unit (FFPU)

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Control of Acyclic Triangle Formations With Orthogonal Control Law

Control of Acyclic Triangle Formations With Orthogonal Contr...

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第二十九届中国控制会议

作者： WANG Hengsheng,WANG Renquan,WU Zhi College of Mechanical&Electrical Engineering,Central South University,Changsha 410083,P.R.China

This paper proposes a distributed control law for three mobile autonomous agents to shape an acyclic triangle *** move themselves autonomously in two orthogonal axes to pursue and maintain the prescribed distances to their *** dynamical system approaches exactly to the desired edge lengths and the orientation of the triangle in any initial condition except initial collocation of agent 1 and *** minimally rigid graphs with more than three agents constructed by the Henneberg sequence,using orthogonal control law leads to the exact formation *** analysis of the dynamical system shows the exponential convergence to the only stable equilibrium which corresponds to the desired formation.A set of three variables is defined to describe floating triangle formation,which neglects the translation and rotation of triangles but not its flipping over in the *** variables are also used as a set of state variables of the floating triangular formation to show the trajectories of the dynamical system.

关键词： Formation Control Acyclic Triangle Formation Distributed Control Law autonomous agents

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