When Demonstrations Meet Generative World Models: A Maximum Likelihood Framework for Offline Inverse Reinforcement Learning

15 February 2023

Papers citing "When Demonstrations Meet Generative World Models: A Maximum Likelihood Framework for Offline Inverse Reinforcement Learning"

9 / 9 papers shown

Title
Reinforcement Learning with Human Feedback: Learning Dynamic Choices via Pessimism Zihao Li Zhuoran Yang Mengdi Wang OffRL 16 51 0 29 May 2023
Maximum-Likelihood Inverse Reinforcement Learning with Finite-Time Guarantees Siliang Zeng Chenliang Li Alfredo García Min-Fong Hong 16 42 0 04 Oct 2022
Identifiability and generalizability from multiple experts in Inverse Reinforcement Learning Paul Rolland Luca Viano Norman Schuerhoff Boris Nikolov V. Cevher OffRL 24 13 0 22 Sep 2022
Training language models to follow instructions with human feedback Long Ouyang Jeff Wu Xu Jiang Diogo Almeida Carroll L. Wainwright ... Amanda Askell Peter Welinder Paul Christiano Jan Leike Ryan J. Lowe OSLM ALM 301 11,730 0 04 Mar 2022
Pessimistic Model-based Offline Reinforcement Learning under Partial Coverage Masatoshi Uehara Wen Sun OffRL 91 144 0 13 Jul 2021
COMBO: Conservative Offline Model-Based Policy Optimization Tianhe Yu Aviral Kumar Rafael Rafailov Aravind Rajeswaran Sergey Levine Chelsea Finn OffRL 197 412 0 16 Feb 2021
A Finite Time Analysis of Two Time-Scale Actor Critic Methods Yue Wu Weitong Zhang Pan Xu Quanquan Gu 85 145 0 04 May 2020
Deep Reinforcement Learning for Autonomous Driving: A Survey B. R. Kiran Ibrahim Sobh V. Talpaert Patrick Mannion A. A. Sallab S. Yogamani P. Pérez 143 1,599 0 02 Feb 2020
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles Balaji Lakshminarayanan Alexander Pritzel Charles Blundell UQCV BDL 268 5,635 0 05 Dec 2016