Real-world problems often require reasoning about hybrid beliefs, over both discrete and continuous random variables. Yet, such a setting has hardly been investigated in the context of planning. Moreover, existing online Partially Observable Markov Decision Processes (POMDPs) solvers do not support hybrid beliefs directly. In particular, these solvers do not address the added computational burden due to an increasing number of hypotheses with the planning horizon, which can grow exponentially. As part of this work, we present a novel algorithm, Hybrid Belief Monte Carlo Planning (HB-MCP) that utilizes the Monte Carlo Tree Search (MCTS) algorithm to solve a POMDP while maintaining a hybrid belief. We illustrate how the upper confidence bound (UCB) exploration bonus can be leveraged to guide the growth of hypotheses trees alongside the belief trees. We then evaluate our approach in highly aliased simulated environments where unresolved data association leads to multi-modal belief hypotheses.
翻译:现实世界的问题往往要求从离散和连续随机变量的角度对混合信仰进行推理。然而,这种背景却很少在规划的背景下得到调查。此外,现有的在线部分可观测的Markov决定程序(POMDPs)解决方案并不直接支持混合信仰。特别是,这些解决方案没有解决由于规划前景中越来越多的假设而增加的计算负担,而这种假设可能会成倍增长。作为这项工作的一部分,我们提出了一个新奇的算法,即混合信仰蒙特卡洛计划(HB-MCP),它利用蒙特卡洛树搜索算法来解决POMDP,同时保持混合信仰。我们说明了如何利用上层信任(UB)勘探奖金来引导假树与信仰树一起生长。然后我们评估了我们高度奇特的模拟环境中的方法,在这些环境中,尚未解决的数据关联导致多模式的信念假想。