Simplified Belief-Dependent Reward MCTS Planning with Guaranteed Tree Consistency

Understand

Partially Observable Markov Decision Processes (POMDPs) are notoriously hard to solve.

• Most advanced state-of-the-art online solvers leverage ideas of Monte Carlo Tree Search (MCTS).
• These solvers rapidly converge to the most promising branches of the belief tree, avoiding the suboptimal sections.
• Most of these algorithms are designed to utilize straightforward access to the state reward and assume the belief-dependent reward is nothing but expectation over the state reward.