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Most of the current algorithms for solving distributed online optimization problems are based on the first-order method, which are simple in computation but slow in convergence. Newton's algorithm with fast convergence speed needs to calculate the Hessian matrix and its inverse, leading to computationally complex. A distributed online optimization algorithm based on Newton's step is proposed in this paper, which constructs a positive definite matrix by using the first-order information of the objective function to replace the inverse of the Hessian matrix in Newton's method. The convergence of the algorithm is proved theoretically and the regret bound of the algorithm is obtained. Finally, numerical experiments are used to verify the feasibility and efficiency of the proposed algorithm. The experimental results show that the proposed algorithm has an efficient performance on practical problems, compared to several existing gradient descent algorithms. [ABSTRACT FROM AUTHOR]

Developing efficient motion policies for multiagents is a challenge in a decentralized dynamic situation, where each agent plans its own paths without knowing the policies of the other agents involved. This paper presents an efficient learning‐based motion planning method for multiagent systems. It adopts the framework of multiagent deep deterministic policy gradient (MADDPG) to directly map partially observed information to motion commands for multiple agents. To improve the efficiency of MADDPG in sample utilization, so as to train more brilliant agents that can adapt to more complex environments, a strategy named mixed experience (ME) is introduced to MADDPG, and this has led to our proposed ME‐MADDPG algorithm. The novel ME strategy can be embodied into three specific mechanisms: (1) an artificial potential field‐based sample generator to produce high‐quality samples in the early training stage; (2) a dynamic mixed sampling strategy to mix the training data from different sources with a variable proportion; (3) a delayed learning skill to stabilize the training of the multiple agents. A series of experiments have been conducted to verify the performance of the proposed ME‐MADDPG algorithm, and it has been demonstrated that, compared with MADDPG, the proposed algorithm can significantly improve the convergence speed and convergence effect in the training process, and it has also shown better efficiency and better adaptability in complex dynamic environments while it is used for multiagent motion planning applications. [ABSTRACT FROM AUTHOR]

We study the distributed resource allocation problem for heterogeneous multiagent systems over an undirected graph, an essential issue in multiagent system coordinated control and complex network system control. The decision variable is subject to global equality and local convex set constraints, and the objective is smooth and convex. It aims to minimize the global objective function by exchanging neighboring information between agents. An adaptive distributed algorithm is designed using the distance function-based exact penalty function method. A state- and time-based triggering condition is designed to avoid continuous communication and reduce the communication burden. From a random initial state, the proposed algorithm asymptotically converges to the optimal value by means of LaSalle's invariance principle. Finally, examples are provided to demonstrate the effectiveness of our algorithm. [ABSTRACT FROM AUTHOR]

Existing investigations of opponent modeling and intention inferencing cannot make clear descriptions and practical explanations of the opponent's behaviors and intentions, which may inevitably limit the applicability of them. In this work, we propose a novel approach for opponent's policy explanation and intention inference based on the behavioral portrait of opponent. Specifically, we use the multiagent deep deterministic policy gradients (MADDPG) algorithm to train the agent and opponent in the competitive environment, and collect the behavioral data of opponent based on agent's observations. Then we perform pattern segmentation and extract the opponent's behavior events via Toeplitz inverse covariance‐based clustering (TICC) algorithm; hence the opponent's behavior data can be encoded into a knowledge graph, named opponent's behavior knowledge graph (OKG). Based on this, we built a question‐answer system (QA system) to query and match opponent historical information in OKG, so that the agent can obtain additional experience and gradually infer the intention of opponent with the episodes of iteration. We evaluate the proposed method on the competitive scenario in multiagent particle environment (MPE). Simulation results show that the agents are able to learn better policies with opponent portrait in competitive settings. [ABSTRACT FROM AUTHOR]

This article focuses on circle formation control problem of multiagent systems based on event-triggered strategy under limited communication bandwidth. In such system, each agent can only perceive the angular distance of its nearest neighbor in the counterclockwise direction, and the angular distance of the nearest neighbor in the clockwise direction needs to be obtained by communicating with each other. In order to address the aforementioned problem, a novel distributed algorithm based on the combination of nonuniform quantitative communication technology and event-triggered control is proposed. Sufficient conditions on circle formation control are derived under which the states of all agents can be confirmed to converge to some desired equilibrium point. Different from the traditional uniform quantization communication framework, nonuniform quantization can be beneficial for handling small signals and improving the performance of multiagent systems concerned. Furthermore, under the proposed policy, all the designed quantizers do not emerge saturated. Numerical simulation results are provided to verify the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]