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 observa- tion data. We characterize the landscape and differentiability of the loss function. Given the difficulty of obtaining the exact gradient, our main contribution is an efficient gradient approximator, which enables a novel inverse feedback game solver that minimizes the loss using first-order optimization. In thorough empirical evaluations, we demonstrate that our algorithm converges reliably and has better robustness and generalization performance than the open-loop baseline method when the observation data reflects a group of players acting in a feedback Nash game.
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Cost Design in Atomic Routing Games
An atomic routing game is a multiplayer game on a directed graph. Each player in the game chooses a path—a sequence of links that connect its origin node to its destination node—with the lowest cost, where the cost of each link is a function of all players’ choices. We develop a novel numerical method to design the link cost function in atomic routing games such that the players’ choices at the Nash equilibrium minimize a given smooth performance function. This method first approximates the nonsmooth Nash equilibrium conditions with smooth ones, then iteratively improves the link cost function via implicit differentiation. We demonstrate the application of this method to atomic routing games that model noncooperative agents navigating in grid worlds.
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- Award ID(s):
- 1652113
- PAR ID:
- 10508428
- Publisher / Repository:
- IEEE
- Date Published:
- Journal Name:
- IEEE Xplore
- ISSN:
- 2378-5861
- ISBN:
- 979-8-3503-2806-6
- Page Range / eLocation ID:
- 1704 to 1709
- Subject(s) / Keyword(s):
- Gradient methods Costs Navigation Design methodology Stochastic processes Games Routing
- Format(s):
- Medium: X
- Location:
- San Diego, CA, USA
- Sponsoring Org:
- National Science Foundation
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