Epistemic Monte Carlo Tree Search
Yaniv Oren Viliam Vadocz Matthijs T. J. Spaan Wendelin Boehmer
The AlphaZero/MuZero (A/MZ) family of algorithms has achieved remarkable success across various challenging domains by integrating Monte Carlo Tree Search (MCTS) with learned models. Learned models introduce epistemic uncertainty, which is caused by learning from limited data and is useful for exploration in sparse reward environments. MCTS does not account for the propagation of this uncertainty however. To address this, we introduce Epistemic MCTS (EMCTS): a theoretically motivated approach to account for the epistemic uncertainty in search and harness the search for deep exploration. In the challenging sparse-reward task of writing code in the Assembly language SUBLEQ, AZ paired with our method achieves significantly higher sample efficiency over baseline AZ. Search with EMCTS 
solves variations of the commonly used hard-exploration benchmark Deep Sea - which baseline A/MZ are practically unable to solve - much faster than an otherwise equivalent method that does not use search for uncertainty estimation, demonstrating significant benefits from search for epistemic uncertainty estimation.
model based, epistemic uncertainty, exploration, planning, alphazero, muzero
We extend A/MZ specifically and algorithms that use MCTS with learned models of value and / or dynamics in general to estimate and propagate epistemic uncertainty in search, and harness the search for deep exploration.