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In this paper, we propose a Q-learning with continuous action policy and extend this algorithm to a multi-agent system. We examine this algorithm in a task that there are two robots taking action independently but connected with a straight bar. The robots must cooperate to move to the goal and avoid the obstacles in the environment. Conventional Q-learning needs a pre-defined and discrete state space...
In this paper a strategy for controlling a group of agents to achieve positional consensus is presented. The proposed technique is based on the constraint that every agents must be given the same control input through a broadcast communication mechanism. Although the control command is computed using state information in a global framework, the control input is implemented by the agents in a local...
We introduce an approach for stable deployment of agents into planar curves (1-D formations in 2-D space) parameterized by the agent index. Stability is ensured by leader feedback, which is designed in a manner similar to boundary control of PDEs. By discretizing the model and the PDE controllers with respect to the continuous agent index, we obtain control laws for the discrete follower agents and...
The goal of this work is to stabilize the shape and orientation of formations of N identical and fully actuated agents, each governed by double-integrator dynamics. Using stability and rigidity properties inherent to tensegrity structures, we first design a tensegrity-based, globally exponentially stable control law in one dimension. This stabilizes given inter-agent spacing along the line, thereby...
This paper formulates a self-organization algorithm to addresses the problem of emergent behavior supervision in engineered swarms of arbitrary population size. Based on collections of independent identical finite-state agents, the algorithm is derived to compute necessary perturbations in the local agents' behavior, which guarantees convergence to the desired observed state of the swarm. A simulation...
In this paper, a novel distributed algorithm to deal with the problem of estimating the network centroid in a multi-agent system is proposed. In this scenario, agents are assumed to be lacking any global reference frame or absolute position information. The proposed algorithm can be thought as a general tool to retrieve information about the centroid of a network of agents. Indeed, this allows to...
This paper presents a modified R-learning according to the traditional average reward reinforcement learning algorithm. Reinforcement learning problems constitute an important class of learning and control problems faced by artificial intelligence systems. The general framework of reinforcement learning can be divided into two forms, discounted reward reinforcement learning and average reward reinforcement...
This paper addresses the problem of coordination path following control of multiple autonomous vehicles. Stated briefly, the problem consists in steering a group of vehicles along a specified paths, while holding a desired inter-ship formation pattern. Path-following for each vehicle amounts to reducing an appropriately defined geometric error to zero. We first show a passivity property for the path...
A basic primitive in a networked robotic swarm is to form a connected component that covers some area with relatively uniform density. Although most approaches to the problem require local coordinate information, it has been proposed that robots with only connectivity information do this instead with a generalized form of diffusion-limited aggregation, in which robots wander randomly until they find...
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