Perturbation-Based Best Arm Identification for Efficient Task Planning with Monte-Carlo Tree Search
Daejong Jin,Juhan Park,Kyungjae Lee,Daejong Jin,Juhan Park,Kyungjae Lee
Combining task and motion planning (TAMP) is crucial for intelligent robots to perform complex and long-horizon tasks. In TAMP, many approaches generally employ Monte-Carlo tree search (MCTS) with upper confidence bound (UCB) for task planning to handle exploration-exploitation trade-off and find globally optimal solutions. However, since UCB basically considers the estimation error caused by nois...
