ResearchTrend.AI
  • Communities
  • Connect sessions
  • AI calendar
  • Organizations
  • Join Slack
  • Contact Sales
Papers
Communities
Social Events
Terms and Conditions
Pricing
Contact Sales
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 1911.05873
  4. Cited By
A Reduction from Reinforcement Learning to No-Regret Online Learning
v1v2 (latest)

A Reduction from Reinforcement Learning to No-Regret Online Learning

International Conference on Artificial Intelligence and Statistics (AISTATS), 2019
14 November 2019
Ching-An Cheng
Rémi Tachet des Combes
Byron Boots
Geoffrey J. Gordon
    OffRL
ArXiv (abs)PDFHTML

Papers citing "A Reduction from Reinforcement Learning to No-Regret Online Learning"

11 / 11 papers shown
Title
Efficiently Solving Discounted MDPs with Predictions on Transition Matrices
Efficiently Solving Discounted MDPs with Predictions on Transition Matrices
Lixing Lyu
Jiashuo Jiang
Wang Chi Cheung
216
3
0
24 Feb 2025
Dealing with unbounded gradients in stochastic saddle-point optimization
Dealing with unbounded gradients in stochastic saddle-point optimization
Gergely Neu
Nneka Okolo
259
5
0
21 Feb 2024
BET: Explaining Deep Reinforcement Learning through The Error-Prone
  Decisions
BET: Explaining Deep Reinforcement Learning through The Error-Prone Decisions
Xiao Liu
Jie Zhao
Wubing Chen
Mao Tan
Yongxin Su
OffRLFAtt
133
0
0
14 Jan 2024
Efficient Global Planning in Large MDPs via Stochastic Primal-Dual
  Optimization
Efficient Global Planning in Large MDPs via Stochastic Primal-Dual OptimizationInternational Conference on Algorithmic Learning Theory (ALT), 2022
Gergely Neu
Nneka Okolo
332
10
0
21 Oct 2022
Proximal Point Imitation Learning
Proximal Point Imitation LearningNeural Information Processing Systems (NeurIPS), 2022
Luca Viano
Angeliki Kamoutsi
Gergely Neu
Igor Krawczuk
Volkan Cevher
352
20
0
22 Sep 2022
Non-Markovian policies occupancy measures
Non-Markovian policies occupancy measures
Romain Laroche
Rémi Tachet des Combes
Jacob Buckman
OffRL
166
1
0
27 May 2022
Slowly Changing Adversarial Bandit Algorithms are Efficient for
  Discounted MDPs
Slowly Changing Adversarial Bandit Algorithms are Efficient for Discounted MDPsInternational Conference on Algorithmic Learning Theory (ALT), 2022
Ian A. Kash
L. Reyzin
Zishun Yu
298
0
0
18 May 2022
Efficient Performance Bounds for Primal-Dual Reinforcement Learning from
  Demonstrations
Efficient Performance Bounds for Primal-Dual Reinforcement Learning from DemonstrationsInternational Conference on Machine Learning (ICML), 2021
Angeliki Kamoutsi
G. Banjac
John Lygeros
OffRL
162
9
0
28 Dec 2021
Near Optimal Policy Optimization via REPS
Near Optimal Policy Optimization via REPSNeural Information Processing Systems (NeurIPS), 2021
Aldo Pacchiano
Jonathan Lee
Peter L. Bartlett
Ofir Nachum
146
3
0
17 Mar 2021
Efficiently Solving MDPs with Stochastic Mirror Descent
Efficiently Solving MDPs with Stochastic Mirror DescentInternational Conference on Machine Learning (ICML), 2020
Yujia Jin
Aaron Sidford
135
76
0
28 Aug 2020
Continuous Online Learning and New Insights to Online Imitation Learning
Continuous Online Learning and New Insights to Online Imitation Learning
Jonathan Lee
Ching-An Cheng
Ken Goldberg
Byron Boots
CLL
87
3
0
03 Dec 2019
1