Cost Inference For Feedback Dynamic Games From Noisy Partial State Observations And Incomplete Trajectories
2023 · Jingqi Li, Chih-Yuan Chiu, Lasse Peters, et al.
Abstract
In multi-agent dynamic games, the Nash equilibrium state trajectory of each agent is determined by its cost function and the information pattern of the game. However, the cost and trajectory of each agent may be unavailable to the other agents. Prior work on using partial observations to infer the costs in dynamic games assumes an open-loop information pattern. In this work, we demonstrate that the feedback Nash equilibrium concept is more expressive and encodes more complex behavior. It is desirable to develop specific tools for inferring players' objectives in feedback games. Therefore, we consider the dynamic game cost inference problem under the feedback information pattern, using only partial state observations and incomplete trajectory data. To this end, we first propose an inverse feedback game loss function, whose minimizer yields a feedback Nash equilibrium state trajectory closest to the observation data. We characterize the landscape and differentiability of the loss functio
Authors
(none)
Tags
Stats
Related papers
- PACE: A Framework For Learning And Control In Linear Incomplete-information Differential Games (2025)0.00
- Peer-aware Cost Estimation In Nonlinear General-sum Dynamic Games For Mutual Learning And Intent Inference (2025)0.00
- Approximate Feedback Nash Equilibria With Sparse Inter-agent Dependencies (2024)0.00
- Generalized Information Gathering Under Dynamics Uncertainty (2026)0.00
- Inverse Optimal Control Adapted To The Noise Characteristics Of The Human Sensorimotor System (2021)0.00
- Probabilistic Inverse Optimal Control For Non-linear Partially Observable Systems Disentangles Perceptual Uncertainty And Behavioral Costs (2023)0.00
- Learning Equilibrium With Estimated Payoffs In Population Games (2024)0.00
- Actor-critic Provably Finds Nash Equilibria Of Linear-quadratic Mean-field Games (2019)0.00