Auto-Encoding Bayesian Inverse Games
CoRR(2024)
摘要
When multiple agents interact in a common environment, each agent's actions
impact others' future decisions, and noncooperative dynamic games naturally
capture this coupling. In interactive motion planning, however, agents
typically do not have access to a complete model of the game, e.g., due to
unknown objectives of other players. Therefore, we consider the inverse game
problem, in which some properties of the game are unknown a priori and must be
inferred from observations. Existing maximum likelihood estimation (MLE)
approaches to solve inverse games provide only point estimates of unknown
parameters without quantifying uncertainty, and perform poorly when many
parameter values explain the observed behavior. To address these limitations,
we take a Bayesian perspective and construct posterior distributions of game
parameters. To render inference tractable, we employ a variational autoencoder
(VAE) with an embedded differentiable game solver. This structured VAE can be
trained from an unlabeled dataset of observed interactions, naturally handles
continuous, multi-modal distributions, and supports efficient sampling from the
inferred posteriors without computing game solutions at runtime. Extensive
evaluations in simulated driving scenarios demonstrate that the proposed
approach successfully learns the prior and posterior objective distributions,
provides more accurate objective estimates than MLE baselines, and facilitates
safer and more efficient game-theoretic motion planning.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要