The olfactory search POMDP (partially observable Markov decision process) is a sequential decision-making problem designed to mimic the task faced by insects searching for a source of odor in turbulence, and its solutions have applications to sniffer robots. As exact solutions are out of reach, the challenge consists in finding the best possible approximate solutions while keeping the computational cost reasonable. We provide a quantitative benchmarking of a solver based on deep reinforcement learning against traditional POMDP approximate solvers. We show that deep reinforcement learning is a competitive alternative to standard methods, in particular to generate lightweight policies suitable for robots.

翻译：摘要：气味搜索部分可观察马尔可夫决策过程（POMDP）是为了模仿昆虫在湍流中寻找气味源的任务而设计的一个序列决策问题，其解决方案可应用于嗅探机器人。由于精确解法难以实现，因此挑战在于在保持计算成本合理的情况下找到最佳的近似解。我们提供了一个基于深度强化学习的求解器相对于传统的POMDP近似求解器的定量基准。我们表明，深度强化学习是标准方法的竞争性替代品，特别是产生适用于机器人的轻量级策略。