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Real-world planning problems, including autonomous driving and sustainable energy applications like carbon storage and resource exploration, have recently been modeled as partially observable Markov decision processes (POMDPs) and solved using approximate methods.
Novel approach to nonlinear/non-Gaussian Bayesian state estimation
Neil J. Gordon, David J. Salmond, and Adrian F.M. Smith · 1993
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Learning policies for partially observable environments: Scaling up
Michael L. Littman, Anthony R. Cassandra, and Leslie Pack Kaelbling · 1995
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An Introduction to the Kalman Filter
Greg Welch and Gary Bishop · 1995
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Planning and acting in partially observable stochastic domains
Leslie Pack Kaelbling, Michael L. Littman, and Anthony R. Cassandra · 1998
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Efficient BackProp
Yann LeCun, Léon Bottou, Genevieve B. Orr, and Klaus-Robert Müller · 2002
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Heuristic Search Value Iteration for POMDPs
Trey Smith and Reid Simmons · 2004
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Finding Approximate POMDP Solutions Through Belief Compression
Nicholas Roy, Geoffrey Gordon, and Sebastian Thrun · 2005
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Probabilistic Robotics
Sebastian Thrun, Wolfram Burgard, and Dieter Fox · 2005
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Bandit Based Monte-Carlo Planning
Levente Kocsis and Csaba Szepesvári · 2006
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Efficient Selectivity and Backup Operators in Monte-Carlo Tree Search
Rémi Coulom · 2007
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Particle Filter-Based Policy Gradient in POMDPs
Pierre-Arnaud Coquelin, Romain Deguest, and Rémi Munos · 2008
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Monte-Carlo Search Techniques in the Modern Board Game Thurn and Taxis
Frederik Christiaan Schadd · 2009
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Approximate Bayesian Computation (ABC) in practice
Katalin Csilléry, Michael G. B. Blum, Oscar E. Gaggiotti, and Olivier François · 2010
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Belief Space Planning Assuming Maximum Likelihood Observations
Robert Platt Jr., Russ Tedrake, Leslie Kaelbling, and Tomas Lozano-Perez · 2010
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Monte-Carlo Planning in Large POMDPs
David Silver and Joel Veness · 2010
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Continuous Upper Confidence Trees
Adrien Couëtoux, Jean-Baptiste Hoock, Nataliya Sokolovska, Olivier Teytaud, and Nicolas Bonnard · 2011
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Multi-Armed Bandits with Episode Context
Christopher D. Rosin · 2011
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A Survey of Monte Carlo Tree Search Methods
Cameron B. Browne, Edward Powley, Daniel Whitehouse, Simon M. Lucas, Peter I. Cowling, Philipp Rohlfshagen, Stephen Tavener, Diego Perez, Spyridon Samothrakis, and Simon Colton · 2012
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Next-Generation Airborne Collision Avoidance System
Mykel J. Kochenderfer, Jessica E. Holland, and James P. Chryssanthacopoulos · 2012
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A survey of point-based POMDP solvers
Guy Shani, Joelle Pineau, and Robert Kaplow · 2013
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Root mean square error (RMSE) or mean absolute error (MAE)?–Arguments against avoiding RMSE in the literature
Tianfeng Chai and Roland R. Draxler · 2014
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Neural Networks for Machine Learning. Lecture 6a: Overview of Mini-Batch Gradient Descent
Geoffrey Hinton, Nitish Srivastava, and Kevin Swersky · 2014
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Adam: A Method for Stochastic Optimization
Diederik P. Kingma and Jimmy Ba · 2014
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Decision Making Under Uncertainty: Theory and Application
Mykel J. Kochenderfer · 2015
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Mastering the game of Go with deep neural networks and tree search
David Silver et al · 2016
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Monte Carlo Tree Search in Continuous Action Spaces with Execution Uncertainty
Timothy Yee, Viliam Lisỳ, Michael H. Bowling, and S. Kambhampati · 2016
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POMDPs.jl: A Framework for Sequential Decision Making under Uncertainty
Maxim Egorov, Zachary N. Sunberg, Edward Balaban, Tim A. Wheeler, Jayesh K. Gupta, and Mykel J. Kochenderfer · 2017
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Mastering the game of Go without human knowledge
HyP-DESPOT: A hybrid parallel algorithm for online planning under uncertainty
Panpan Cai, Yuanfu Luo, David Hsu, and Wee Sun Lee · 2021
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Improving AlphaZero Using Monte-Carlo Graph Search
Johannes Czech, Patrick Korus, and Kristian Kersting · 2021
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Learning and Planning in Complex Action Spaces
Thomas Hubert, Julian Schrittwieser, Ioannis Antonoglou, Mohammadamin Barekatain, Simon Schmitt, and David Silver · 2021
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Improved POMDP Tree Search Planning with Prioritized Action Branching
John Mern, Anil Yildiz, Lawrence Bush, Tapan Mukerji, and Mykel J. Kochenderfer · 2021
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Vector Quantized Models for Planning
Sherjil Ozair, Yazhe Li, Ali Razavi, Ioannis Antonoglou, Aaron Van Den Oord, and Oriol Vinyals · 2021
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Monte Carlo Tree Search with Iteratively Refining State Abstractions
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David Silver et al · 2017
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DESPOT: Online POMDP Planning with Regularization
Nan Ye, Adhiraj Somani, David Hsu, and Wee Sun Lee · 2017
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Deep Sets
Manzil Zaheer, Satwik Kottur, Siamak Ravanbakhsh, et al · 2017
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Deep Variational Reinforcement Learning for POMDPs
Maximilian Igl, Luisa Zintgraf, Tuan Anh Le, Frank Wood, and Shimon Whiteson · 2018
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A0C: Alpha Zero in Continuous Action Space
Thomas M. Moerland, Joost Broekens, Aske Plaat, and Catholijn M. Jonker · 2018
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A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play
David Silver et al · 2018
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Online Algorithms for POMDPs with Continuous State, Action, and Observation Spaces
Zachary N. Sunberg and Mykel J. Kochenderfer · 2018
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Samuel Sokota, Caleb Y. Ho, Zaheen Ahmad, and J. Zico Kolter · 2021
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POMDPs for Safe Visibility Reasoning in Autonomous Vehicles
Kyle Hollins Wray, Bernard Lange, Arec Jamgochian, Stefan J. Witwicki, Atsuhide Kobashi, Sachin Hagaribommanahalli, and David Ilstrup · 2021
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Closing the Planning–Learning Loop With Application to Autonomous Driving
Panpan Cai and David Hsu · 2022
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Flow-Based Recurrent Belief State Learning for POMDPs
Xiaoyu Chen, Yao Mark Mu, Ping Luo, Shengbo Li, and Jianyu Chen · 2022
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A POMDP Model for Safe Geological Carbon Sequestration
Anthony Corso, Yizheng Wang, Markus Zechner, Jef Caers, and Mykel J. Kochenderfer · 2022
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Policy Improvement by Planning with Gumbel
Ivo Danihelka, Arthur Guez, Julian Schrittwieser, and David Silver · 2022
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Root-mean-square error (RMSE) or mean absolute error (MAE): When to use them or not
Timothy O. Hodson · 2022
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Algorithms for Decision Making
Mykel J. Kochenderfer, Tim A. Wheeler, and Kyle H. Wray · 2022
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Partially Observable Markov Decision Processes in Robotics: A Survey
Mikko Lauri, David Hsu, and Joni Pajarinen · 2022
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A Unified Survey on Anomaly, Novelty, Open-Set, and Out of-Distribution Detection: Solutions and Future Challenges
Mohammadreza Salehi, Hossein Mirzaei, Dan Hendrycks, Yixuan Li, Mohammad Hossein Rohban, and Mohammad Sabokrou · 2022
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Approximate Information State for Approximate Planning and Reinforcement Learning in Partially Observed Systems
Jayakumar Subramanian et al · 2022
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A sequential decision-making framework with uncertainty quantification for groundwater management
Yizheng Wang, Markus Zechner, John Michael Mern, Mykel J. Kochenderfer, and Jef Karel Caers · 2022
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Optimality Guarantees for Particle Belief Approximation of POMDPs
Michael H. Lim, Tyler J. Becker, Mykel J. Kochenderfer, Claire J. Tomlin, and Zachary N. Sunberg · 2023
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Learning Logic Specifications for Soft Policy Guidance in POMCP
Giulio Mazzi, Daniele Meli, Alberto Castellini, and Alessandro Farinelli · 2023
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The Intelligent Prospector v1.0: Geoscientific Model Development and Prediction by Sequential Data Acquisition Planning with Application to Mineral Exploration
John Mern and Jef Caers · 2023
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Optimizing Carbon Storage Operations for Long-Term Safety
Yizheng Wang, Markus Zechner, Gege Wen, Anthony Louis Corso, John Michael Mern, Mykel J. Kochenderfer, and Jef Karel Caers · 2023
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