Inferring Objectives in Continuous Dynamic Games from Noise-Corrupted Partial State Observations

TL;DR

This paper introduces a probabilistic inverse game solver that infers agents' objectives from noisy, partial observations in dynamic games, enabling trajectory prediction without full state information.

Contribution

It presents a novel method that jointly estimates player objectives and states using Nash equilibrium constraints, robustly handling noise and partial observations.

Findings

Accurately infers objectives from noisy, partial data

Predicts agent trajectories effectively using estimated objectives

Operates without requiring full state or strategy observations

Abstract

Robots and autonomous systems must interact with one another and their environment to provide high-quality services to their users. Dynamic game theory provides an expressive theoretical framework for modeling scenarios involving multiple agents with differing objectives interacting over time. A core challenge when formulating a dynamic game is designing objectives for each agent that capture desired behavior. In this paper, we propose a method for inferring parametric objective models of multiple agents based on observed interactions. Our inverse game solver jointly optimizes player objectives and continuous-state estimates by coupling them through Nash equilibrium constraints. Hence, our method is able to directly maximize the observation likelihood rather than other non-probabilistic surrogate criteria. Our method does not require full observations of game states or player strategies to identify player objectives. Instead, it robustly recovers this information from noisy, partial state observations. As a byproduct of estimating player objectives, our method computes a Nash equilibrium trajectory corresponding to those objectives. Thus, it is suitable for downstream trajectory forecasting tasks. We demonstrate our method in several simulated traffic scenarios. Results show that it reliably estimates player objectives from a short sequence of noise-corrupted partial state observations. Furthermore, using the estimated objectives, our method makes accurate predictions of each player's trajectory.