Discovering and Removing Exogenous State Variables and Rewards for
Reinforcement Learning

Discovering and Removing Exogenous State Variables and Rewards for Reinforcement Learning

5 June 2018

Thomas G. Dietterich

George Trimponias

Papers citing "Discovering and Removing Exogenous State Variables and Rewards for Reinforcement Learning"

9 / 9 papers shown

Title
Mean-Field Sampling for Cooperative Multi-Agent Reinforcement Learning Emile Anand Ishani Karmarkar Guannan Qu 88 1 0 01 Dec 2024
Online Reinforcement Learning in Non-Stationary Context-Driven Environments Pouya Hamadanian Arash Nasr-Esfahany Malte Schwarzkopf Siddartha Sen MohammadIman Alizadeh CLL OffRL 55 0 0 04 Feb 2023
Guaranteed Discovery of Control-Endogenous Latent States with Multi-Step Inverse Models Alex Lamb Riashat Islam Yonathan Efroni Aniket Didolkar Dipendra Kumar Misra Dylan J. Foster Lekan Molu Rajan Chari A. Krishnamurthy John Langford 51 24 0 17 Jul 2022
Hindsight Learning for MDPs with Exogenous Inputs Sean R. Sinclair Felipe Vieira Frujeri Ching-An Cheng Luke Marshall Hugo Barbalho Jingling Li Jennifer Neville Ishai Menache Adith Swaminathan 18 23 0 13 Jul 2022
Sample-Efficient Reinforcement Learning in the Presence of Exogenous Information Yonathan Efroni Dylan J. Foster Dipendra Kumar Misra A. Krishnamurthy John Langford OffRL 31 25 0 09 Jun 2022
The Sandbox Environment for Generalizable Agent Research (SEGAR) R. Devon Hjelm Bogdan Mazoure Florian Golemo Felipe Vieira Frujeri Mihai Jalobeanu Andrey Kolobov LLMAG LRM 24 1 0 19 Mar 2022
Exploiting Action Impact Regularity and Exogenous State Variables for Offline Reinforcement Learning Vincent Liu James Wright Martha White OffRL 35 1 0 15 Nov 2021
Learning to drive from a world on rails Di Chen V. Koltun Philipp Krahenbuhl 98 116 0 03 May 2021
Learning Compact Models for Planning with Exogenous Processes Rohan Chitnis Tomás Lozano-Pérez 27 20 0 30 Sep 2019