ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2103.04289
  4. Cited By
Learning Human Rewards by Inferring Their Latent Intelligence Levels in
  Multi-Agent Games: A Theory-of-Mind Approach with Application to Driving Data

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
Ran Tian
Masayoshi Tomizuka
Liting Sun
    AI4CE
ArXiv (abs)PDFHTML

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"

3 / 3 papers shown
Title
AToM: Adaptive Theory-of-Mind-Based Human Motion Prediction in Long-Term Human-Robot Interactions
AToM: Adaptive Theory-of-Mind-Based Human Motion Prediction in Long-Term Human-Robot Interactions
Yuwen Liao
Muqing Cao
Xinhang Xu
Lihua Xie
135
0
0
09 Feb 2025
Multi-Agent Inverse Reinforcement Learning: Suboptimal Demonstrations
  and Alternative Solution Concepts
Multi-Agent Inverse Reinforcement Learning: Suboptimal Demonstrations and Alternative Solution Concepts
S. Bergerson
AI4CE
53
3
0
02 Sep 2021
Deep Multiagent Reinforcement Learning: Challenges and Directions
Deep Multiagent Reinforcement Learning: Challenges and Directions
Annie Wong
Thomas Bäck
Anna V. Kononova
Aske Plaat
AI4CE
116
97
0
29 Jun 2021
1