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