论文标题
蒙特卡洛计划中的混合信念pomdps
Monte Carlo Planning in Hybrid Belief POMDPs
论文作者
论文摘要
现实世界中的问题通常需要在离散和连续的随机变量上进行有关混合信念的推理。但是，在计划的背景下，几乎没有对这种设置进行调查。此外，现有的在线可观察到的马尔可夫决策过程（POMDP）求解器不直接支持混合信念。特别是，由于规划范围的假设越来越多，这些求解器并未解决增加的计算负担，这些假设可以成倍增长。作为这项工作的一部分，我们提出了一种新颖的算法，混合信念蒙特卡洛计划（HB-MCP），它利用蒙特卡洛树搜索（MCTS）算法来解决POMDP，同时保持混合信念。我们说明了如何利用上限信心（UCB）探索奖金来指导假设树木与信仰树一起的生长。然后，我们评估我们在高叠叠的模拟环境中的方法，在这些环境中未解决的数据关联导致多模式信念假设。
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.
