Ronald E. Parr

发表

Deep Radial-Basis Value Functions for Continuous Control

Kavosh Asadi, Michael L. Littman, Neev Parikh, 2021, AAAI.

Approximating Optimal Policies for Partially Observable Stochastic Domains

Stuart J. Russell, Ronald Parr, Ronald E. Parr, 1995, IJCAI.

An analysis of linear models, linear value-function approximation, and feature selection for reinforcement learning

Lihong Li, Michael L. Littman, Gavin Taylor, 2008, ICML '08.

Reinforcement Learning as Classification: Leveraging Modern Classifiers

Michail G. Lagoudakis, Ronald Parr, Ronald E. Parr, 2003, ICML.

Reinforcement Learning with Hierarchies of Machines

Stuart J. Russell, Ronald Parr, Ronald E. Parr, 1997, NIPS.

A Novel Benchmark Methodology and Data Repository for Real-life Reinforcement Learning

Ronald E. Parr, M. Littman, Lihong Li, 2009 .

Coordinated Reinforcement Learning

Michail G. Lagoudakis, Carlos Guestrin, Ronald Parr, 2002, ICML.

Solving Factored POMDPs with Linear Value Functions

Ronald E. Parr, D. Koller, Carlos Guestrin, 2001 .

Model-Free Least-Squares Policy Iteration

Michail G. Lagoudakis, Ronald Parr, Ronald E. Parr, 2001, NIPS.

Least-Squares Policy Iteration

Michail G. Lagoudakis, Ronald Parr, Ronald E. Parr, 2003, J. Mach. Learn. Res..

Generalized Prioritized Sweeping

David Andre, Nir Friedman, Ronald Parr, 1997, NIPS.

Hierarchical control and learning for markov decision processes

Ronald E. Parr, Ronald E. Parr, 1998 .

Least-Squares Methods in Reinforcement Learning for Control

Michail G. Lagoudakis, Michael L. Littman, Ronald Parr, 2002, SETN.

Max-norm Projections for Factored MDPs

Carlos Guestrin, Daphne Koller, Ronald Parr, 2001, IJCAI.

Policy Iteration for Factored MDPs

Daphne Koller, Ronald Parr, Ronald E. Parr, 2000, UAI.

Computing Factored Value Functions for Policies in Structured MDPs

Daphne Koller, Ronald Parr, Ronald E. Parr, 1999, IJCAI.

L1 Regularized Linear Temporal Difference Learning

Ronald E. Parr, Christopher Painter-Wakefield, 2012 .

Reinforcement Learning Using Approximate Belief States

Daphne Koller, Ronald Parr, Andres C. Rodriguez, 1999, NIPS.

Flexible Decomposition Algorithms for Weakly Coupled Markov Decision Problems pdf

Ronald Parr, Ronald E. Parr, 1998, UAI.

Multiagent Planning with Factored MDPs

Carlos Guestrin, Daphne Koller, Ronald Parr, 2001, NIPS.

Provably Bounded Optimal Agents

Devika Subramanian, Stuart J. Russell, Ronald Parr, 1993, IJCAI.

Value Function Approximation in Zero-Sum Markov Games

Michail G. Lagoudakis, Ronald Parr, Ronald E. Parr, 2002, UAI.

Kernelized value function approximation for reinforcement learning

Gavin Taylor, Ronald Parr, Ronald E. Parr, 2009, ICML '09.

DP-SLAM: Fast, Robust Simultaneous Localization and Mapping Without Predetermined Landmarks

Ronald Parr, Austin I. Eliazar, Ronald E. Parr, 2003, IJCAI.

Bayesian Fault Detection and Diagnosis in Dynamic Systems

Gautam Biswas, Uri Lerner, Daphne Koller, 2000, AAAI/IAAI.

Inference in Hybrid Networks: Theoretical Limits and Practical Algorithms

Uri Lerner, Ronald Parr, Ronald E. Parr, 2001, UAI.

Greedy Algorithms for Sparse Reinforcement Learning

Ronald Parr, Christopher Painter-Wakefield, Ronald E. Parr, 2012, ICML.

Point-Based Policy Iteration

Hui Li, Lawrence Carin, Shihao Ji, 2007, AAAI.

Solving Stackelberg games with uncertain observability

Vincent Conitzer, Ronald Parr, Dmytro Korzhyk, 2011, AAMAS.

Multi-Step Multi-Sensor Hider-Seeker Games

Vincent Conitzer, Ronald Parr, Erik Halvorson, 2009, IJCAI.

Hierarchical Linear/Constant Time SLAM Using Particle Filters for Dense Maps

Ronald Parr, Austin I. Eliazar, Ronald E. Parr, 2005, NIPS.

Selecting the Right Algorithm

Michail G. Lagoudakis, Michael L. Littman, Ronald Parr, 2001 .

Non-Myopic Multi-Aspect Sensing with Partially Observable Markov Decision Processes

Ronald E. Parr, L. Carin, Shihao Ji, 2005 .

Learning probabilistic motion models for mobile robots

Ronald Parr, Austin I. Eliazar, Ronald E. Parr, 2004, ICML.

Textured occupancy grids for monocular localization without features

Ronald Parr, Julian Mason, Susanna Ricco, 2011, 2011 IEEE International Conference on Robotics and Automation.

Computing Stackelberg strategies in stochastic games

Vincent Conitzer, Joshua Letchford, Ronald Parr, 2012, SECO.

A Unifying Framework for Temporal Abstraction in Stochastic Processes

Ronald E. Parr, 1998 .

Making Rational Decisions Using Adaptive Utility Elicitation

Daphne Koller, Ronald Parr, Urszula Chajewska, 2000, AAAI/IAAI.

Distance Minimization for Reward Learning from Scored Trajectories

Carlo Tomasi, Ronald Parr, Benjamin Burchfiel, 2016, AAAI.

Computing Optimal Strategies to Commit to in Stochastic Games

Vincent Conitzer, Joshua Letchford, Ronald Parr, 2012, AAAI.

Nonmyopic Multiaspect Sensing With Partially Observable Markov Decision Processes

Lawrence Carin, Shihao Ji, Ronald Parr, 2007, IEEE Transactions on Signal Processing.

CS-2012-01 L 1 Regularized Linear Temporal Difference Learning

Ronald E. Parr, Christopher Painter-Wakefield, 2012 .

Solving for Lipschitz Continuous Value Functions through Induced MDPs

Ronald E. Parr, Balaraman Ravindran, Dhanvin Mehta, 2015 .

On the Existence of Simpler Machine Learning Models

Ronald E. Parr, C. Rudin, Lesia Semenova, 2019, FAccT.

Deep RBF Value Functions for Continuous Control pdf

Kavosh Asadi, Michael L. Littman, Ronald E. Parr, 2020, ArXiv.

Fitted Q-Learning for Relational Domains pdf

Kristian Kersting, Kaushik Roy, Sriraam Natarajan, 2020, ArXiv.

Learning in Zero-Sum Team Markov Games Using Factored Value Functions

Michail G. Lagoudakis, Ronald Parr, Ronald E. Parr, 2002, NIPS.

Approximate Policy Iteration using Large-Margin Classifiers

Michail G. Lagoudakis, Ronald Parr, Ronald E. Parr, 2003, IJCAI.

Improving PAC Exploration Using the Median Of Means

Jonathan P. How, Jason Pazis, Ronald Parr, 2016, NIPS.

Regionalized Policy Representation for Reinforcement Learning in POMDPs

Hui Li, Lawrence Carin, Ronald Parr, 2007 .

Introduction to Approximation Algorithms

Ronald E. Parr, 2016 .

Efficient Solution Algorithms for Factored MDPs

Shobha Venkataraman, Carlos Guestrin, Daphne Koller, 2003, J. Artif. Intell. Res..

Complexity of Computing Optimal Stackelberg Strategies in Security Resource Allocation Games

Vincent Conitzer, Ronald Parr, Dmytro Korzhyk, 2010, AAAI.

Revisiting the Softmax Bellman Operator: Theoretical Properties and Practical Benefits pdf

Lawrence Carin, Zhao Song, Ronald E. Parr, 2018, ArXiv.

Analyzing feature generation for value-function approximation

Lihong Li, Michael L. Littman, Ronald Parr, 2007, ICML '07.

Sparse value function approximation for reinforcement learning

Ronald Parr, Christopher Painter-Wakefield, Ronald E. Parr, 2013 .

Non-Parametric Approximate Linear Programming for MDPs

Jason Pazis, Ronald Parr, Ronald E. Parr, 2011, AAAI.

Linear Feature Encoding for Reinforcement Learning

Lawrence Carin, Zhao Song, Xuejun Liao, 2016, NIPS.

Security Games with Multiple Attacker Resources

Vincent Conitzer, Ronald Parr, Dmytro Korzhyk, 2011, IJCAI.

Efficient PAC-Optimal Exploration in Concurrent, Continuous State MDPs with Delayed Updates

Jason Pazis, Ronald Parr, Ronald E. Parr, 2016, AAAI.

Value Function Approximation in Noisy Environments Using Locally Smoothed Regularized Approximate Linear Programs pdf

Gavin Taylor, Ronald Parr, Ronald E. Parr, 2012, UAI.

Linear Complementarity for Regularized Policy Evaluation and Improvement

Ronald Parr, Jeffrey Johns, Christopher Painter-Wakefield, 2010, NIPS.

Feature Selection Using Regularization in Approximate Linear Programs for Markov Decision Processes

Marek Petrik, Shlomo Zilberstein, Gavin Taylor, 2010, ICML.

PAC Optimal Exploration in Continuous Space Markov Decision Processes

Jason Pazis, Ronald Parr, Ronald E. Parr, 2013, AAAI.

Policy Search via Density Estimation

Andrew Y. Ng, Daphne Koller, Ronald Parr, 1999, NIPS.

Revisiting the Softmax Bellman Operator: New Benefits and New Perspective

Lawrence Carin, Zhao Song, Ronald E. Parr, 2018, ICML.

Efficient Selection of Disambiguating Actions for Stereo Vision pdf

Ronald Parr, Monika Schaeffer, Ronald E. Parr, 2006, UAI.

On-Line Selectivity Estimation for XML Path Expressions using Markov Histograms

Sriram Padmanabhan, Lipyeow Lim, Min Wang, 2005 .