We propose Stable Yet Memory Bounded Open-Loop (SYMBOL) planning, a general
memory bounded approach to partially observable open-loop planning. SYMBOL
maintains an adaptive stack of Thompson Sampling bandits, whose size is bounded
by the planning horizon and can be automatically adapted according to the
underlying domain without any prior domain knowledge beyond a generative model.
We empirically test SYMBOL in four large POMDP benchmark problems to
demonstrate its effectiveness and robustness w.r.t. the choice of
hyperparameters and evaluate its adaptive memory consumption. We also compare
its performance with other open-loop planning algorithms and POMCP.

提出一种名为 SYMBOL 的规划方法，该方法采用稳定但内存有限的方法处理部分可观测的开环规划，通过自适应使用 Thompson Sampling bandits 堆栈实现，适用于不需要先前领域知识和面向各种领域的问题，同时在四个大型 POMDP 基准问题中获得了良好的效果和稳健性能。

适应性汤普森抽样堆栈用于内存有限的开环规划

Adaptive Thompson Sampling Stacks for Memory Bounded Open-Loop Planning

State-of-the-art approaches to partially observable planning like POMCP are
based on stochastic tree search. While these approaches are computationally
efficient, they may still construct search trees of considerable size, which
could limit the performance due to restricted memory resources. In this paper,
we propose Partially Observable Stacked Thompson Sampling (POSTS), a memory
bounded approach to open-loop planning in large POMDPs, which optimizes a fixed
size stack of Thompson Sampling bandits. We empirically evaluate POSTS in four
large benchmark problems and compare its performance with different tree-based
approaches. We show that POSTS achieves competitive performance compared to
tree-based open-loop planning and offers a performance-memory tradeoff, making
it suitable for partially observable planning with highly restricted
computational and memory resources.

提出了一种基于限制记忆的开环规划方法 —— 部分可观察堆叠 Thompson 采样（POSTS），用以解决大型 POMDP 问题中内存资源有限的问题，相较于传统的基于树的规划方法，该方法具有性能和内存之间的平衡，四个基准问题的实验表明 POSTS 具有较好的竞争表现。