Learning Successor States and Goal-Dependent Values: A Mathematical Viewpoint

18 January 2021

Papers citing "Learning Successor States and Goal-Dependent Values: A Mathematical Viewpoint"

10 / 10 papers shown

Title
Temporal Representation Alignment: Successor Features Enable Emergent Compositionality in Robot Instruction Following Vivek Myers Bill Chunyuan Zheng Anca Dragan Kuan Fang Sergey Levine 58 0 0 08 Feb 2025
Exploration by Learning Diverse Skills through Successor State Measures Paul-Antoine Le Tolguenec Yann Besse Florent Teichteil-Königsbuch Dennis G. Wilson Emmanuel Rachelson 30 0 0 14 Jun 2024
Ensemble Successor Representations for Task Generalization in Offline-to-Online Reinforcement Learning Changhong Wang Xudong Yu Chenjia Bai Qiaosheng Zhang Zhen Wang 38 1 0 12 May 2024
Zero-Shot Reinforcement Learning from Low Quality Data Scott Jeen Tom Bewley Jonathan M. Cullen OffRL OnRL 27 0 0 26 Sep 2023
A Novel Stochastic Gradient Descent Algorithm for Learning Principal Subspaces Charline Le Lan Joshua Greaves Jesse Farebrother Mark Rowland Fabian Pedregosa Rishabh Agarwal Marc G. Bellemare 29 8 0 08 Dec 2022
Contrastive Value Learning: Implicit Models for Simple Offline RL Bogdan Mazoure Benjamin Eysenbach Ofir Nachum Jonathan Tompson SSL OffRL 15 7 0 03 Nov 2022
Does Zero-Shot Reinforcement Learning Exist? Ahmed Touati Jérémy Rapin Yann Ollivier OffRL 12 38 0 29 Sep 2022
Contrastive Learning as Goal-Conditioned Reinforcement Learning Benjamin Eysenbach Tianjun Zhang Ruslan Salakhutdinov Sergey Levine SSL OffRL 23 137 0 15 Jun 2022
Provable Benefits of Representational Transfer in Reinforcement Learning Alekh Agarwal Yuda Song Wen Sun Kaiwen Wang Mengdi Wang Xuezhou Zhang OffRL 10 33 0 29 May 2022
Learning One Representation to Optimize All Rewards Ahmed Touati Yann Ollivier OffRL 19 59 0 14 Mar 2021