论文标题
成本注意的异步多代理主动搜索
Cost Aware Asynchronous Multi-Agent Active Search
论文作者
论文摘要
多代理主动搜索要求自主代理选择有效定位目标的传感操作。在现实的环境中，代理商还必须考虑其决定所产生的成本。先前提出的主动搜索算法通过使用近视决策和/或忽略的成本来忽略代理商环境中的不确定性，从而简化了问题。在本文中，我们引入了一种在线主动搜索算法，通过对代理商的行为做出自适应的成本感知决策，以在未知环境中检测目标。我们的算法结合了汤普森采样（用于搜索空间探索和分散的多代理决策），蒙特卡洛树搜索（用于长时间的地平线规划）和帕累托（Pareto-Optimal-Optimal-Offimal-Offimal Profestion）的范围（用于在未知环境中的多目标优化），以提出一个在线lookahead策划者，以提出所有简化所有简化的计划。我们分析该算法在模拟中的性能，以显示其在成本意识主动搜索中的功效。
Multi-agent active search requires autonomous agents to choose sensing actions that efficiently locate targets. In a realistic setting, agents also must consider the costs that their decisions incur. Previously proposed active search algorithms simplify the problem by ignoring uncertainty in the agent's environment, using myopic decision making, and/or overlooking costs. In this paper, we introduce an online active search algorithm to detect targets in an unknown environment by making adaptive cost-aware decisions regarding the agent's actions. Our algorithm combines principles from Thompson Sampling (for search space exploration and decentralized multi-agent decision making), Monte Carlo Tree Search (for long horizon planning) and pareto-optimal confidence bounds (for multi-objective optimization in an unknown environment) to propose an online lookahead planner that removes all the simplifications. We analyze the algorithm's performance in simulation to show its efficacy in cost aware active search.
