AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
This work proposes a modified Multi-Agent Twin-Delayed Deep Deterministic Policy Gradient (M-MATD3) algorithm, specifically designed to mitigate common issues such as overestimation bias and high variance observed in standard MATD3.
Decentralized multi-robot navigation is difficult when robots must act from local observations without centralized coordination or explicit inter-robot communication. A belief-driven hybrid reinforcement learning framework is evaluated for planar multi-robot navigation under partial observability. Each robot builds a c...
Feature-fusion Multi-Agent Proximal Policy Optimization (FMAPPO) is proposed, which fuses 2D LiDAR measurements with task-specific state information to enable safe decentralized multi-robot task assignment and navigation and demonstrates that the learned decentralized policies can coordinate multiple robots to service...
The proposed Proximal Policy Optimization-based deep reinforcement learning approach for real-time obstacle avoidance for mobile robots is a successful and computationally feasible solution to real-time robotic navigation in dynamic environments.
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...
Extensive simulations and real-world experiments demonstrate that the proposed framework can efficiently generate and iteratively improve motion plans for different planning objectives, robotic platforms, and swarm configurations, highlighting its effectiveness, computational efficiency, and scalability as a general pl...
This follow-up work tests the feasibility of the neuro-inspired self-supervised learning framework for trajectory planning that leverages forward and inverse models as the internal supervisory mechanism in an environment that contains an obstacle, and demonstrates the tendency of the planner to exploit the learning sig...
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