6,066 research outputs found
Identification of Hessian matrix in distributed gradient-based multi-agent coordination control systems
Multi-agent coordination control usually involves a potential function that
encodes information of a global control task, while the control input for
individual agents is often designed by a gradient-based control law. The
property of Hessian matrix associated with a potential function plays an
important role in the stability analysis of equilibrium points in
gradient-based coordination control systems. Therefore, the identification of
Hessian matrix in gradient-based multi-agent coordination systems becomes a key
step in multi-agent equilibrium analysis. However, very often the
identification of Hessian matrix via the entry-wise calculation is a very
tedious task and can easily introduce calculation errors. In this paper we
present some general and fast approaches for the identification of Hessian
matrix based on matrix differentials and calculus rules, which can easily
derive a compact form of Hessian matrix for multi-agent coordination systems.
We also present several examples on Hessian identification for certain typical
potential functions involving edge-tension distance functions and
triangular-area functions, and illustrate their applications in the context of
distributed coordination and formation control
Route Swarm: Wireless Network Optimization through Mobility
In this paper, we demonstrate a novel hybrid architecture for coordinating
networked robots in sensing and information routing applications. The proposed
INformation and Sensing driven PhysIcally REconfigurable robotic network
(INSPIRE), consists of a Physical Control Plane (PCP) which commands agent
position, and an Information Control Plane (ICP) which regulates information
flow towards communication/sensing objectives. We describe an instantiation
where a mobile robotic network is dynamically reconfigured to ensure high
quality routes between static wireless nodes, which act as source/destination
pairs for information flow. The ICP commands the robots towards evenly
distributed inter-flow allocations, with intra-flow configurations that
maximize route quality. The PCP then guides the robots via potential-based
control to reconfigure according to ICP commands. This formulation, deemed
Route Swarm, decouples information flow and physical control, generating a
feedback between routing and sensing needs and robotic configuration. We
demonstrate our propositions through simulation under a realistic wireless
network regime.Comment: 9 pages, 4 figures, submitted to the IEEE International Conference on
Intelligent Robots and Systems (IROS) 201
Counterfactual Multi-Agent Policy Gradients
Cooperative multi-agent systems can be naturally used to model many real
world problems, such as network packet routing and the coordination of
autonomous vehicles. There is a great need for new reinforcement learning
methods that can efficiently learn decentralised policies for such systems. To
this end, we propose a new multi-agent actor-critic method called
counterfactual multi-agent (COMA) policy gradients. COMA uses a centralised
critic to estimate the Q-function and decentralised actors to optimise the
agents' policies. In addition, to address the challenges of multi-agent credit
assignment, it uses a counterfactual baseline that marginalises out a single
agent's action, while keeping the other agents' actions fixed. COMA also uses a
critic representation that allows the counterfactual baseline to be computed
efficiently in a single forward pass. We evaluate COMA in the testbed of
StarCraft unit micromanagement, using a decentralised variant with significant
partial observability. COMA significantly improves average performance over
other multi-agent actor-critic methods in this setting, and the best performing
agents are competitive with state-of-the-art centralised controllers that get
access to the full state
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