Discrete Factorial Representations as an Abstraction for Goal Conditioned Reinforcement Learning

1 November 2022

Papers citing "Discrete Factorial Representations as an Abstraction for Goal Conditioned Reinforcement Learning"

9 / 9 papers shown

Title
Mitigating Adversarial Perturbations for Deep Reinforcement Learning via Vector Quantization Tung M. Luu Thanh Nguyen Tee Joshua Tian Jin Sungwoon Kim Chang D. Yoo AAML 30 0 0 04 Oct 2024
Query-based Semantic Gaussian Field for Scene Representation in Reinforcement Learning Jiaxu Wang Ziyi Zhang Qiang Zhang Jia Li Jingkai Sun Mingyuan Sun Junhao He Renjing Xu 3DGS 39 3 0 04 Jun 2024
LAGMA: LAtent Goal-guided Multi-Agent Reinforcement Learning Hyungho Na IL-Chul Moon 45 1 0 30 May 2024
Behavior Generation with Latent Actions Seungjae Lee Yibin Wang Haritheja Etukuru H. J. Kim Mahi Shafiullah Lerrel Pinto VGen OffRL 35 66 0 05 Mar 2024
Learning Top-k Subtask Planning Tree based on Discriminative Representation Pre-training for Decision Making Jingqing Ruan Kaishen Wang Qingyang Zhang Dengpeng Xing Bo Xu 33 0 0 18 Dec 2023
CQM: Curriculum Reinforcement Learning with a Quantized World Model Seungjae Lee Daesol Cho Jonghae Park H. J. Kim 36 6 0 26 Oct 2023
Modularity in Deep Learning: A Survey Haozhe Sun Isabelle Guyon MoMe 40 2 0 02 Oct 2023
SNeRL: Semantic-aware Neural Radiance Fields for Reinforcement Learning D. Shim Seungjae Lee H. J. Kim 40 15 0 27 Jan 2023
Decoupling Representation Learning from Reinforcement Learning Adam Stooke Kimin Lee Pieter Abbeel Michael Laskin SSL DRL 286 341 0 14 Sep 2020