逆強化學習於多機器人資訊軌跡規劃

The AI community has been paying more attention to multi-robot informative path planning (MIPP). MIPP is to plan trajectories for robots to maximize information gathering. If the robots are cooperative, potential applications include cooperative map exploration, cooperative search and cooperative disinfection etc. If robots are adversarial, potential applications include pursuit evasion games. However, finding optimal solutions for the aforementioned problems are NP-hard. Hence, this research proposes an inverse reinforcement learning approach to improveMIPP performance of robots through imitate how humans solve MIPP problems in daily lives (e.g., cooperative search). To make a breakthrough of the MIPP research status, this research analyzes MIPP problems through adaptive submodularity, topology, and adversary. To consider the state uncertainty, the adaptive submodularity of MIPP will be explored. To consider the invariant property, the topology of MIPP will be explored. To consider the adversarial status, the adversary of MIPP will be explored.The goal of this research is to explore some issues of MIPP:(1) When the targets and environments are probabilistic, could the MIPP has theoretical guarantees?(2) When the targets and environments are dynamic, what’s the invariant property for MIPP problems?(3) When the targets are adversarial, could the MIPP has theoretical guarantees?(4) When the targets execute adversarial attack, could the MIPP has theoretical guarantees?