Sensing noise and complex decision-making pose critical challenges to active search for lost persons amid disasters, impeding efficient rescue efforts. We introduce CARL, a collaborative altitude-adaptive reinforcement learning framework for UAV swarms. CARL integrates confidence-informed assessment with Sparse Bayesian Learning to diminish the noise impact on sensor performance, and an altitude-adaptive planner for collaborative active search strategy. Simulation experiments with up to 50 targets and 10 UAVs demonstrate CARL’s superior performance compared to baseline methods in lost person active search scenarios.

Utilizing unmanned aerial vehicles (UAVs) as mobile access points can assist urban communication systems in establishing emergency networks in disaster scenarios. However, in large-scale dynamic environments, the extensive exploration space makes effective collaboration among a large number of UAVs challenging. In this paper, to schedule the deployment of UAVs for networking purposes, we propose a novel approach, MAEN, using multi-agent reinforcement learning. The grouping and information sharing mechanisms in MAEN enable the algorithm to easily scale up the number of UAVs to dozens and address the issue of strategy equilibrium. Additionally, a reward decomposition module is designed to handle coordination and task allocation among UAVs. Experimental results demonstrate that the algorithm outperforms existing algorithms in terms of ground device coverage and communication quality.

Building agents based on tree-search planning capabilities with learned models has achieved remarkable success in classic decision-making problems, such as Go and Atari.However, it has been deemed challenging or even infeasible to extend Monte Carlo Tree Search (MCTS) based algorithms to diverse real-world applications, especially when these environments involve complex action spaces and significant simulation costs, or inherent stochasticity.In this work, we introduce LightZero, the first unified benchmark for deploying MCTS/MuZero in general sequential decision scenarios. Specificially, we summarize the most critical challenges in designing a general MCTS-style decision-making solver, then decompose the tightly-coupled algorithm and system design of tree-search RL methods into distinct sub-modules.By incorporating more appropriate exploration and optimization strategies, we can significantly enhance these sub-modules and construct powerful LightZero agents to tackle tasks across a wide range of domains, such as board games, Atari, MuJoCo, MiniGrid and GoBigger.Detailed benchmark results reveal the significant potential of such methods in building scalable and efficient decision intelligence.The code is available as part of OpenDILab at https://github.com/opendilab/LightZero.