Near Optimal Behavior via Approximate State Abstraction

15 January 2017

Michael L. Littman

Papers citing "Near Optimal Behavior via Approximate State Abstraction"

37 / 87 papers shown

Title
Reinforcement Learning with Subspaces using Free Energy Paradigm Milad Ghorbani Reshad Hosseini Seyed Pooya Shariatpanahi M. N. Ahmadabadi 121 0 0 13 Dec 2020
Domain Concretization from Examples: Addressing Missing Domain Knowledge via Robust Planning Akshay Sharma Piyush Rajesh Medikeri Yu Zhang 20 1 0 18 Nov 2020
Loss Bounds for Approximate Influence-Based Abstraction E. Congeduti A. Mey F. Oliehoek 63 9 0 03 Nov 2020
Approximate information state for approximate planning and reinforcement learning in partially observed systems Jayakumar Subramanian Amit Sinha Raihan Seraj Aditya Mahajan 155 86 0 17 Oct 2020
Randomized Value Functions via Posterior State-Abstraction Sampling Dilip Arumugam Benjamin Van Roy OffRL 85 7 0 05 Oct 2020
Planning with Learned Object Importance in Large Problem Instances using Graph Neural Networks Tom Silver Rohan Chitnis Aidan Curtis J. Tenenbaum Tomas Lozano-Perez L. Kaelbling GNN 93 80 0 11 Sep 2020
CAMPs: Learning Context-Specific Abstractions for Efficient Planning in Factored MDPs Rohan Chitnis Tom Silver Beomjoon Kim L. Kaelbling Tomas Lozano-Perez 73 27 0 26 Jul 2020
Approximation Benefits of Policy Gradient Methods with Aggregated States Daniel Russo 123 7 0 22 Jul 2020
Provably Safe PAC-MDP Exploration Using Analogies Melrose Roderick Vaishnavh Nagarajan J. Zico Kolter 25 12 0 07 Jul 2020
Environment Shaping in Reinforcement Learning using State Abstraction Parameswaran Kamalaruban R. Devidze Volkan Cevher Adish Singla OffRL 51 4 0 23 Jun 2020
ELSIM: End-to-end learning of reusable skills through intrinsic motivation A. Aubret L. Matignon S. Hassas 52 5 0 23 Jun 2020
Continuous Action Reinforcement Learning from a Mixture of Interpretable Experts R. Akrour Davide Tateo Jan Peters 60 22 0 10 Jun 2020
Task-Oriented Data Compression for Multi-Agent Communications Over Bit-Budgeted Channels Arsham Mostaani T. Vu Symeon Chatzinotas Björn E. Ottersten 67 11 0 28 May 2020
Learning Transferable Concepts in Deep Reinforcement Learning Diego Gomez Nicanor Quijano Luis Felipe Giraldo CLL SSL 31 0 0 16 May 2020
TOMA: Topological Map Abstraction for Reinforcement Learning Zhao-Heng Yin Wu-Jun Li 37 2 0 11 May 2020
Exchangeable Input Representations for Reinforcement Learning John Mern Dorsa Sadigh Mykel J. Kochenderfer 107 4 0 19 Mar 2020
Learning Discrete State Abstractions With Deep Variational Inference Ondrej Biza Robert Platt Jan-Willem van de Meent Lawson L. S. Wong BDL 81 12 0 09 Mar 2020
Adaptive Temporal Difference Learning with Linear Function Approximation Tao Sun Han Shen Tianyi Chen Dongsheng Li 77 23 0 20 Feb 2020
Learning State Abstractions for Transfer in Continuous Control Kavosh Asadi David Abel Michael L. Littman OffRL 63 7 0 08 Feb 2020
Explainable Artificial Intelligence (XAI) for 6G: Improving Trust between Human and Machine Weisi Guo 69 40 0 11 Nov 2019
Green Deep Reinforcement Learning for Radio Resource Management: Architecture, Algorithm Compression and Challenge Zhiyong Du Yansha Deng Weisi Guo A. Nallanathan Qi-hui Wu 34 30 0 11 Oct 2019
Model-Based Reinforcement Learning Exploiting State-Action Equivalence Mahsa Asadi M. S. Talebi Hippolyte Bourel Odalric-Ambrym Maillard OffRL 101 9 0 09 Oct 2019
Online Planning with Lookahead Policies Yonathan Efroni Mohammad Ghavamzadeh Shie Mannor 48 5 0 10 Sep 2019
A Sufficient Statistic for Influence in Structured Multiagent Environments F. Oliehoek Stefan J. Witwicki L. Kaelbling 81 23 0 22 Jul 2019
DeepMDP: Learning Continuous Latent Space Models for Representation Learning Carles Gelada Saurabh Kumar Jacob Buckman Ofir Nachum Marc G. Bellemare BDL 99 290 0 06 Jun 2019
Object Exchangeability in Reinforcement Learning: Extended Abstract John Mern Dorsa Sadigh Mykel Kochenderfer OCL 51 1 0 07 May 2019
From Abstractions to Grounded Languages for Robust Coordination of Task Planning Robots Yu-an Zhang 53 1 0 01 May 2019
Information-Theoretic Considerations in Batch Reinforcement Learning Jinglin Chen Nan Jiang OOD OffRL 188 378 0 01 May 2019
A Geometric Perspective on Optimal Representations for Reinforcement Learning Marc G. Bellemare Will Dabney Robert Dadashi Adrien Ali Taïga Pablo Samuel Castro Nicolas Le Roux Dale Schuurmans Tor Lattimore Clare Lyle 67 90 0 31 Jan 2019
Successor Features Combine Elements of Model-Free and Model-based Reinforcement Learning Lucas Lehnert Michael L. Littman 103 10 0 31 Jan 2019
Performance Guarantees for Homomorphisms Beyond Markov Decision Processes Sultan Javed Majeed Marcus Hutter 23 9 0 09 Nov 2018
Near-Optimal Representation Learning for Hierarchical Reinforcement Learning Ofir Nachum S. Gu Honglak Lee Sergey Levine 91 211 0 02 Oct 2018
Approximate Exploration through State Abstraction Adrien Ali Taïga Aaron Courville Marc G. Bellemare 65 13 0 29 Aug 2018
The Bottleneck Simulator: A Model-based Deep Reinforcement Learning Approach Iulian Serban Chinnadhurai Sankar Michael Pieper Joelle Pineau Yoshua Bengio OffRL 96 29 0 12 Jul 2018
On overfitting and asymptotic bias in batch reinforcement learning with partial observability Vincent François-Lavet Guillaume Rabusseau Joelle Pineau D. Ernst R. Fonteneau OffRL 88 34 0 22 Sep 2017
Unsupervised Basis Function Adaptation for Reinforcement Learning Edward W. Barker C. Ras OffRL 47 3 0 03 Mar 2017
Agent-Agnostic Human-in-the-Loop Reinforcement Learning David Abel J. Salvatier Andreas Stuhlmuller Owain Evans 88 62 0 15 Jan 2017