Online Planning for Interactive-POMDPs using Nested Monte Carlo Tree Search
Jonathon Schwartz,Ruijia Zhou,Hanna Kurniawati,Jonathon Schwartz,Ruijia Zhou,Hanna Kurniawati
The ability to make good decisions in partially observed non-cooperative multi-agent scenarios is important for robots to interact effectively in human environments. A robust framework for such decision-making problems is the Interactive Partially Observable Markov Decision Processes (I-POMDPs), which explicitly models the other agents' beliefs up to a finite reasoning level in order to more accur...
