AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
This work presents a decentralized approach to solving the multi-agent path problem, allowing each agent to dynamically interact with the environment and make decisions based on information within its localFOV.
This work formulate large-scale MAPP as a partially observable networked Markov decision process as a decentralized model-based Actor-Critic using the Kronecker-factored trust region (DM-ACKTR) algorithm, which consistently obtains the highest TCR and lowest CR.
An efficient way to resolve the curse of dimensionality, improve obstacle avoidance and cooperative formation control of UAVs was found and shows great prospects of practical application in such domains as military operations, search and rescue missions, transport automation and disaster management.
This work proposes a distributed algorithm that enables agents to collectively traverse an unknown, possibly cyclic graph and proves that the algorithm is complete, that its makespan is asymptotically equivalent to that of an optimal full-knowledge strategy, and derive its time and space complexity.
A novel distributed hybrid coordination framework that integrates macroscopic consensus-based task allocation with localized, dynamic conflict resolution strategies and is highly resilient and viable for high-density, real-world autonomous logistics infrastructures is presented.
This work introduces a unified RL formulation that jointly optimizes agent and environment policies, where the environment policy learns graph edge costs to provide global movement guidance via backward Dijkstra search and achieves significant improvements over the strong search-based planner, Causal-PIBT, across multi...
A new diffusion method for multi-agent planning with STL specifications is introduced, making the approach generalizable to novel formulas whose predicates are placed anywhere within the goal region covered during training, while achieving the same scalability as existing learning-based methods.
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