ResearchTrend.AI
  • Communities
  • Connect sessions
  • AI calendar
  • Organizations
  • Join Slack
  • Contact Sales
Papers
Communities
Social Events
Terms and Conditions
Pricing
Contact Sales
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2026 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2005.01138
  4. Cited By
Off-Policy Adversarial Inverse Reinforcement Learning

Off-Policy Adversarial Inverse Reinforcement Learning

3 May 2020
Samin Yeasar Arnob
ArXiv (abs)PDFHTML

Papers citing "Off-Policy Adversarial Inverse Reinforcement Learning"

7 / 7 papers shown
On Reward Transferability in Adversarial Inverse Reinforcement Learning: Insights from Random Matrix Theory
On Reward Transferability in Adversarial Inverse Reinforcement Learning: Insights from Random Matrix Theory
Yangchun Zhang
Wang Zhou
Yirui Zhou
243
0
0
31 Dec 2024
Rethinking Adversarial Inverse Reinforcement Learning: Policy Imitation,
  Transferable Reward Recovery and Algebraic Equilibrium Proof
Rethinking Adversarial Inverse Reinforcement Learning: Policy Imitation, Transferable Reward Recovery and Algebraic Equilibrium Proof
Yangchun Zhang
Qiang Liu
Weiming Li
Yirui Zhou
327
0
0
21 Mar 2024
A Simple Solution for Offline Imitation from Observations and Examples
  with Possibly Incomplete Trajectories
A Simple Solution for Offline Imitation from Observations and Examples with Possibly Incomplete TrajectoriesNeural Information Processing Systems (NeurIPS), 2023
Kai Yan
Alex Schwing
Yu-Xiong Wang
OffRL
289
6
0
02 Nov 2023
Classifying Ambiguous Identities in Hidden-Role Stochastic Games with
  Multi-Agent Reinforcement Learning
Classifying Ambiguous Identities in Hidden-Role Stochastic Games with Multi-Agent Reinforcement LearningAutonomous Agents and Multi-Agent Systems (AAMAS), 2022
Shijie Han
Siyuan Li
Bo An
Wei Zhao
P. Liu
174
1
0
24 Oct 2022
Weighted Maximum Entropy Inverse Reinforcement Learning
Weighted Maximum Entropy Inverse Reinforcement Learning
Viet The Bui
Tien Mai
Patrick Jaillet
125
0
0
20 Aug 2022
Importance of Empirical Sample Complexity Analysis for Offline
  Reinforcement Learning
Importance of Empirical Sample Complexity Analysis for Offline Reinforcement Learning
Samin Yeasar Arnob
Riashat Islam
Doina Precup
OffRL
125
7
0
31 Dec 2021
OPIRL: Sample Efficient Off-Policy Inverse Reinforcement Learning via
  Distribution Matching
OPIRL: Sample Efficient Off-Policy Inverse Reinforcement Learning via Distribution MatchingIEEE International Conference on Robotics and Automation (ICRA), 2021
Hanako Hoshino
Keita Ota
Asako Kanezaki
Rio Yokota
OffRLOOD
160
20
0
09 Sep 2021
1
Page 1 of 1