Learning Human Rewards by Inferring Their Latent Intelligence Levels in Multi-Agent Games: A Theory-of-Mind Approach with Application to Driving Data

7 March 2021

Papers citing "Learning Human Rewards by Inferring Their Latent Intelligence Levels in Multi-Agent Games: A Theory-of-Mind Approach with Application to Driving Data"

7 / 7 papers shown

Title
AToM: Adaptive Theory-of-Mind-Based Human Motion Prediction in Long-Term Human-Robot Interactions Yuwen Liao Muqing Cao Xinhang Xu Lihua Xie 77 0 0 09 Feb 2025
Expressing Diverse Human Driving Behavior with Probabilistic Rewards and Online Inference Liting Sun Zheng Wu Hengbo Ma Masayoshi Tomizuka OffRL 37 7 0 20 Aug 2020
Active Task-Inference-Guided Deep Inverse Reinforcement Learning F. Memarian Zhe Xu Bo Wu Min Wen Ufuk Topcu 33 26 0 24 Jan 2020
Multi-Agent Adversarial Inverse Reinforcement Learning Lantao Yu Jiaming Song Stefano Ermon 51 134 0 30 Jul 2019
Probabilistic Prediction of Interactive Driving Behavior via Hierarchical Inverse Reinforcement Learning Liting Sun Wei Zhan Masayoshi Tomizuka 49 114 0 09 Sep 2018
Courteous Autonomous Cars Liting Sun Wei Zhan Masayoshi Tomizuka Anca Dragan 38 70 0 08 Aug 2018
A Connection between Generative Adversarial Networks, Inverse Reinforcement Learning, and Energy-Based Models Chelsea Finn Paul Christiano Pieter Abbeel Sergey Levine OffRL AI4CE GAN 62 354 0 11 Nov 2016