Epistemic Monte Carlo Tree Search for Deep Exploration in Reinforcement Learning

This paper introduces Epistemic Monte Carlo Tree Search (EMCTS), a novel algorithm that enhances traditional Monte Carlo Tree Search (MCTS) by incorporating epistemic uncertainty for more efficient exploration in reinforcement learning, particularly in sparse-reward environments.