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Online and offline learning of player objectives from partial observations in dynamic games.

, , , , , , and . Int. J. Robotics Res., 42 (10): 917-937 (September 2023)

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Learning Players' Objectives in Continuous Dynamic Games from Partial State Observations., , , , , , and . CoRR, (2023)Inference-Based Strategy Alignment for General-Sum Differential Games., , , and . AAMAS, page 1037-1045. International Foundation for Autonomous Agents and Multiagent Systems, (2020)Cost Inference for Feedback Dynamic Games from Noisy Partial State Observations and Incomplete Trajectories., , , , , and . AAMAS, page 1062-1070. ACM, (2023)Efficient Iterative Linear-Quadratic Approximations for Nonlinear Multi-Player General-Sum Differential Games., , , , and . ICRA, page 1475-1481. IEEE, (2020)Learning to Play Trajectory Games Against Opponents With Unknown Objectives., , and . IEEE Robotics Autom. Lett., 8 (7): 4139-4146 (July 2023)Contingency Games for Multi-Agent Interaction., , , , , , and . CoRR, (2023)Designing Convolutional Neural Networks Using a Genetic Approach for Ball Detection., , , , and . RoboCup, volume 11374 of Lecture Notes in Computer Science, page 150-161. Springer, (2018)Auto-Encoding Bayesian Inverse Games., , , , and . CoRR, (2024)iLQGames.jl: Rapidly Designing and Solving Differential Games in Julia., and . CoRR, (2020)Learning Mixed Strategies in Trajectory Games., , , , , and . Robotics: Science and Systems, (2022)