信念空間における確率的動的ゲーム
Stochastic Dynamic Games in Belief Space
不確実性下で他エージェントと相互作用するためのゲーム理論的POMDPをガウス信念空間のiLQGで解き、追跡・誘導・レースに応用した研究。
著者: Wilko Schwarting, Alyssa Pierson, Sertac Karaman, Daniela Rus
分類: cs.RO, cs.GT, cs.MA
原文アブストラクト
Information gathering while interacting with other agents under sensing and motion uncertainty is critical in domains such as driving, service robots, racing, or surveillance. The interests of agents may be at odds with others, resulting in a stochastic non-cooperative dynamic game. Agents must predict others' future actions without communication, incorporate their actions into these predictions, account for uncertainty and noise in information gathering, and consider what information their actions reveal. Our solution uses local iterative dynamic programming in Gaussian belief space to solve a game-theoretic continuous POMDP. Solving a quadratic game in the backward pass of a game-theoretic belief-space variant of iLQG achieves a runtime polynomial in the number of agents and linear in the planning horizon. Our algorithm yields linear feedback policies for our robot, and predicted feedback policies for other agents. We present three applications: active surveillance, guiding eyes for a blind agent, and autonomous racing. Agents with game-theoretic belief-space planning win 44% more races than without game theory and 34% more than without belief-space planning.
