Learning Markov State Abstractions for Deep Reinforcement Learning

Learning Markov State Abstractions for Deep Reinforcement Learning

8 June 2021

Cameron Allen

George Konidaris

Papers citing "Learning Markov State Abstractions for Deep Reinforcement Learning"

8 / 8 papers shown

Title
Studying the Interplay Between the Actor and Critic Representations in Reinforcement Learning Samuel Garcin Trevor A. McInroe P. S. Castro Prakash Panangaden Christopher G. Lucas David Abel Stefano V. Albrecht 51 0 0 08 Mar 2025
On shallow planning under partial observability Randy Lefebvre Audrey Durand OffRL 28 0 0 22 Jul 2024
Effective Reinforcement Learning Based on Structural Information Principles Xianghua Zeng Hao Peng Dingli Su Angsheng Li 40 0 0 15 Apr 2024
Bridging State and History Representations: Understanding Self-Predictive RL Tianwei Ni Benjamin Eysenbach Erfan Seyedsalehi Michel Ma Clement Gehring Aditya Mahajan Pierre-Luc Bacon AI4TS AI4CE 22 20 0 17 Jan 2024
Conditional Mutual Information for Disentangled Representations in Reinforcement Learning Mhairi Dunion Trevor A. McInroe K. Luck Josiah P. Hanna Stefano V. Albrecht OOD DRL 20 17 0 23 May 2023
Hierarchical State Abstraction Based on Structural Information Principles Xianghua Zeng Hao Peng Angsheng Li Chunyang Liu Lifang He Philip S. Yu 18 16 0 24 Apr 2023
Meta-Learning Parameterized Skills Haotian Fu Shangqun Yu Saket Tiwari Michael Littman G. Konidaris 24 6 0 07 Jun 2022
Decoupling Representation Learning from Reinforcement Learning Adam Stooke Kimin Lee Pieter Abbeel Michael Laskin SSL DRL 284 339 0 14 Sep 2020