Stochastic Dynamic Games in Belief Space

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 noncooperative 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 iterative linear-quadratic Gaussian control (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.

Automated summary

Gathering information while interacting with other agents in environments with sensing and motion uncertainty is critical in various domains. Agents must predict others' future actions without communication, account for uncertainty and noise in information gathering, and consider what information their actions reveal. Our algorithm, using local iterative dynamic programming in Gaussian belief space, achieves a runtime polynomial in the number of agents and linear in the planning horizon, providing linear feedback policies for our robot.