動的ゲームにおける部分観測からのプレイヤー目的のオンライン・オフライン学習
Online and Offline Learning of Player Objectives from Partial Observations in Dynamic Games
ノイズを含む部分的な状態観測から、連続動的ゲームにおける各プレイヤーの目的（コストパラメータ）をナッシュ均衡制約を通じて推定する手法を提案し、交通シナリオで有効性を示した。
著者: Lasse Peters, Vicenç Rubies-Royo, Claire J. Tomlin, Laura Ferranti, Javier Alonso-Mora, Cyrill Stachniss, David Fridovich-Keil
分類: cs.RO
原文アブストラクト
Robots deployed to the real world must be able to interact with other agents in their environment. Dynamic game theory provides a powerful mathematical framework for modeling scenarios in which agents have individual objectives and interactions evolve over time. However, a key limitation of such techniques is that they require a-priori knowledge of all players' objectives. In this work, we address this issue by proposing a novel method for learning players' objectives in continuous dynamic games from noise-corrupted, partial state observations. Our approach learns objectives by coupling the estimation of unknown cost parameters of each player with inference of unobserved states and inputs through Nash equilibrium constraints. By coupling past state estimates with future state predictions, our approach is amenable to simultaneous online learning and prediction in receding horizon fashion. We demonstrate our method in several simulated traffic scenarios in which we recover players' preferences for, e.g., desired travel speed and collision-avoidance behavior. Results show that our method reliably estimates game-theoretic models from noise-corrupted data that closely matches ground-truth objectives, consistently outperforming state-of-the-art approaches.
