# Adaptive Thompson Sampling Stacks for Memory Bounded Open-Loop Planning

arXiv: https://arxiv.org/abs/1907.05861 · HTML: https://mlsanity.com/paper/1907.05861/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jul 2019.

## Built on by (most cited first, 1 of 1)

- [Adaptive Anytime Multi-Agent Path Finding Using Bandit-Based Large Neighborhood Search](https://mlsanity.com/paper/2312.16767/) (2023)

## Abstract (arXiv)

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.