46,780 research outputs found
Randomized Optimal Consensus of Multi-agent Systems
In this paper, we formulate and solve a randomized optimal consensus problem
for multi-agent systems with stochastically time-varying interconnection
topology. The considered multi-agent system with a simple randomized iterating
rule achieves an almost sure consensus meanwhile solving the optimization
problem \min_{z\in \mathds{R}^d}\ \sum_{i=1}^n f_i(z), in which the optimal
solution set of objective function can only be observed by agent
itself. At each time step, simply determined by a Bernoulli trial, each agent
independently and randomly chooses either taking an average among its neighbor
set, or projecting onto the optimal solution set of its own optimization
component. Both directed and bidirectional communication graphs are studied.
Connectivity conditions are proposed to guarantee an optimal consensus almost
surely with proper convexity and intersection assumptions. The convergence
analysis is carried out using convex analysis. We compare the randomized
algorithm with the deterministic one via a numerical example. The results
illustrate that a group of autonomous agents can reach an optimal opinion by
each node simply making a randomized trade-off between following its neighbors
or sticking to its own opinion at each time step
MotionLM: Multi-Agent Motion Forecasting as Language Modeling
Reliable forecasting of the future behavior of road agents is a critical
component to safe planning in autonomous vehicles. Here, we represent
continuous trajectories as sequences of discrete motion tokens and cast
multi-agent motion prediction as a language modeling task over this domain. Our
model, MotionLM, provides several advantages: First, it does not require
anchors or explicit latent variable optimization to learn multimodal
distributions. Instead, we leverage a single standard language modeling
objective, maximizing the average log probability over sequence tokens. Second,
our approach bypasses post-hoc interaction heuristics where individual agent
trajectory generation is conducted prior to interactive scoring. Instead,
MotionLM produces joint distributions over interactive agent futures in a
single autoregressive decoding process. In addition, the model's sequential
factorization enables temporally causal conditional rollouts. The proposed
approach establishes new state-of-the-art performance for multi-agent motion
prediction on the Waymo Open Motion Dataset, ranking 1st on the interactive
challenge leaderboard.Comment: To appear at the International Conference on Computer Vision (ICCV)
202
Multiagent cooperation for solving global optimization problems: an extendible framework with example cooperation strategies
This paper proposes the use of multiagent cooperation for solving global optimization problems through the introduction of a new multiagent environment, MANGO. The strength of the environment lays in itsflexible structure based on communicating software agents that attempt to solve a problem cooperatively. This structure allows the execution of a wide range of global optimization algorithms described as a set of interacting operations. At one extreme, MANGO welcomes an individual non-cooperating agent, which is basically the traditional way of solving a global optimization problem. At the other extreme, autonomous agents existing in the environment cooperate as they see fit during run time. We explain the development and communication tools provided in the environment as well as examples of agent realizations and cooperation scenarios. We also show how the multiagent structure is more effective than having a single nonlinear optimization algorithm with randomly selected initial points
Cost Adaptation for Robust Decentralized Swarm Behaviour
Decentralized receding horizon control (D-RHC) provides a mechanism for
coordination in multi-agent settings without a centralized command center.
However, combining a set of different goals, costs, and constraints to form an
efficient optimization objective for D-RHC can be difficult. To allay this
problem, we use a meta-learning process -- cost adaptation -- which generates
the optimization objective for D-RHC to solve based on a set of human-generated
priors (cost and constraint functions) and an auxiliary heuristic. We use this
adaptive D-RHC method for control of mesh-networked swarm agents. This
formulation allows a wide range of tasks to be encoded and can account for
network delays, heterogeneous capabilities, and increasingly large swarms
through the adaptation mechanism. We leverage the Unity3D game engine to build
a simulator capable of introducing artificial networking failures and delays in
the swarm. Using the simulator we validate our method on an example coordinated
exploration task. We demonstrate that cost adaptation allows for more efficient
and safer task completion under varying environment conditions and increasingly
large swarm sizes. We release our simulator and code to the community for
future work.Comment: Accepted to IEEE/RSJ International Conference on Intelligent Robots
and Systems (IROS), 201
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