| Revisiting the Softmax Bellman Operator: New Benefits and New Perspective | 0 | 0.34 | 2019 |
| Revisiting the Softmax Bellman Operator: Theoretical Properties and Practical Benefits. | 0 | 0.34 | 2018 |
| Efficient PAC-Optimal Exploration in Concurrent, Continuous State MDPs with Delayed Updates. | 6 | 0.51 | 2016 |
| Distance Minimization for Reward Learning from Scored Trajectories. | 5 | 0.44 | 2016 |
| Improving PAC Exploration Using the Median Of Means. | 0 | 0.34 | 2016 |
| Linear Feature Encoding for Reinforcement Learning. | 0 | 0.34 | 2016 |
| PAC Optimal Exploration in Continuous Space Markov Decision Processes. | 19 | 0.83 | 2013 |
| Sample Complexity and Performance Bounds for Non-Parametric Approximate Linear Programming. | 1 | 0.36 | 2013 |
| Policy Iteration for Factored MDPs | 50 | 7.84 | 2013 |
| Value function approximation in zero-sum markov games | 16 | 0.94 | 2013 |
| Computing Optimal Strategies to Commit to in Stochastic Games. | 9 | 0.61 | 2012 |
| Greedy Algorithms for Sparse Reinforcement Learning. | 21 | 0.89 | 2012 |
| Value Function Approximation in Noisy Environments Using Locally Smoothed Regularized Approximate Linear Programs | 4 | 0.52 | 2012 |
| Computing Stackelberg strategies in stochastic games | 3 | 0.42 | 2012 |
| Security games with multiple attacker resources | 26 | 1.18 | 2011 |
| Generalized Value Functions for Large Action Sets. | 11 | 0.66 | 2011 |
| Solving Stackelberg games with uncertain observability | 30 | 2.03 | 2011 |
| Efficient solution algorithms for factored MDPs | 151 | 7.21 | 2011 |
| Non-Parametric Approximate Linear Programming for MDPs. | 10 | 0.69 | 2011 |
| Counting Objects with a Combination of Horizontal and Overhead Sensors | 0 | 0.34 | 2010 |
| Feature Selection Using Regularization in Approximate Linear Programs for Markov Decision Processes | 32 | 1.58 | 2010 |
| Linear Complementarity for Regularized Policy Evaluation and Improvement. | 25 | 1.38 | 2010 |
| Multi-step multi-sensor hider-seeker games | 30 | 2.40 | 2009 |
| Kernelized value function approximation for reinforcement learning | 47 | 1.66 | 2009 |
| An analysis of linear models, linear value-function approximation, and feature selection for reinforcement learning | 65 | 2.48 | 2008 |
| Planning Aims for a Network of Horizontal and Overhead Sensors | 1 | 0.41 | 2008 |
| Nonmyopic Multiaspect Sensing With Partially Observable Markov Decision Processes | 18 | 0.95 | 2007 |
| Point-Based Policy Iteration | 10 | 0.61 | 2007 |
| Analyzing feature generation for value-function approximation | 59 | 3.22 | 2007 |
| Efficient Selection of Disambiguating Actions for Stereo Vision | 0 | 0.34 | 2006 |
| Hierarchical Linear/Constant Time SLAM Using Particle Filters for Dense Maps | 23 | 1.23 | 2005 |
| Learning probabilistic motion models for mobile robots | 30 | 2.06 | 2004 |
| DP-SLAM 2.0 | 56 | 3.10 | 2004 |
| Reinforcement Learning as Classification: Leveraging Modern Classifiers | 70 | 4.98 | 2003 |
| Least-squares policy iteration | 519 | 25.74 | 2003 |
| DP-SLAM: fast, robust simultaneous localization and mapping without predetermined landmarks | 123 | 8.62 | 2003 |
| Approximate policy iteration using large-margin classifiers | 0 | 0.34 | 2003 |
| Coordinated Reinforcement Learning | 80 | 4.77 | 2002 |
| Learning in Zero-Sum Team Markov Games Using Factored Value Functions | 5 | 0.49 | 2002 |
| XPathLearner: an on-line self-tuning Markov histogram for XML path selectivity estimation | 49 | 1.89 | 2002 |
| Least-Squares Methods in Reinforcement Learning for Control | 26 | 1.99 | 2002 |
| Model-Free Least-Squares Policy Iteration | 40 | 3.68 | 2001 |
| Max-norm projections for factored MDPs | 57 | 6.76 | 2001 |
| Multiagent Planning with Factored MDPs | 115 | 8.59 | 2001 |
| Making Rational Decisions Using Adaptive Utility Elicitation | 146 | 12.05 | 2000 |
| Computing Factored Value Functions for Policies in Structured MDPs | 70 | 9.50 | 1999 |
| Reinforcement learning with hierarchies of machines | 221 | 18.14 | 1997 |
| Generalized Prioritized Sweeping. | 0 | 0.34 | 1997 |
| Approximating optimal policies for partially observable stochastic domains | 66 | 21.65 | 1995 |
| Provably bounded-optimal agents | 83 | 9.08 | 1995 |
