- Learning to Take Concurrent Actions Khashayar Rohanimanesh
- Spatial and Temporal Abstraction of POMDPs Applied to Robot Navigation Spatial and Temporal Abstractions in POMDPs Applied to
- Hierarchical MultiAgent Reinforcement Learning Rajbala Makar
- A Reinforcement Learning Model of Selective Visual Silviu Minut
- Optimizing Production Manufacturing using Reinforcement Learning Sridhar Mahadevan and Georgios Theocharous
- Learning Locality-Preserving Discriminative Chang Wang and Sridhar Mahadevan
- Compressing POMDPs using Locality Preserving Non-Negative Matrix Factorization
- A General Framework for Manifold Alignment Chang Wang and Sridhar Mahadevan
- Foundations and Trends R Machine Learning
- Multiscale Dimensionality Reduction Based on Diffusion Wavelets
- Journal of Machine Learning Research 8 (2007) 2629-2669 Submitted 6/03; Revised 8/07; Published 11/07 Hierarchical Average Reward Reinforcement Learning
- Learning To Plan Using Harmonic Analysis Of Diffusion Models Sridhar Mahadevan and Sarah Osentoski and Jeff Johns and Kimberly Ferguson and Chang Wang
- Compact Spectral Bases for Value Function Approximation Using Kronecker Factorization
- Value Function Approximation with Diffusion Wavelets and Laplacian Eigenfunctions
- New Frontiers in Representation Discovery
- A Multiscale Framework for Markov Decision Processes using Diffusion Wavelets
- Journal of Machine Learning Research 8 (2007) 2169-2231 Submitted 6/06; Revised 3/07; Published 9/07 Proto-value Functions: A Laplacian Framework for Learning
- Discrete Event Dynamic Systems: Theory and Applications, 13, 4177, 2003 # 2003 Kluwer Academic Publishers. Manufactured in The Netherlands.
- STUDENT PAPER: A Multiagent Reinforcement Learning Algorithm by Dynamically Merging Markov Decision
- Decision-Theoretic Planning with Concurrent Temporally Extended Khashayar Rohanimanesh
- Hierarchical Optimization of PolicyCoupled SemiMarkov Decision Processes
- APPROXIMATE DYNAMIC PROGRAMMING
- Representation Discovery in Planning using Harmonic Analysis Jeff Johns and Sarah Osentoski and Sridhar Mahadevan
- Spatiotemporal Abstraction of Stochastic Sequential Processes
- Hybrid Least-Squares Algorithms for Approximate Policy Evaluation
- Constructing Basis Functions from Directed Graphs for Value Function Approximation
- Solving SemiMarkov Decision Problems using Average Reward Reinforcement Learning
- Approximate Planning with Hierarchical Partially Observable Markov Decision Process Models for
- SelfImproving Factory Simulation using Continuoustime AverageReward Reinforcement Learning
- Improving Intelligent Tutoring Systems: Using Expectation Maximization to Learn Student
- Continuous-Time Hierarchical Reinforcement Learning Mohammad Ghavamzadeh ghavamza@cse.msu.edu
- Hierarchical Reinforcement Learning Using Graphical Models Victoria Manfredi vmanfred@cs.umass.edu
- An Expert System for Assigning Patients into Clinical Trials Based on Bayesian Networks
- Dynamic Abstraction Networks Victoria Manfredi, Sridhar Mahadevan
- Hierarchically Optimal Average Reward Reinforcement Learning Mohammad Ghavamzadeh mgh@cs.umass.edu
- Machine Learning for Robots: A Comparison of Different Paradigms
- Designing Agent Controllers using DiscreteEvent Markov Models Sridhar Mahadevan and Nikfar Khaleeli \Lambda
- Machine Learning, ?, 1--22 (1998) fl 1998 Kluwer Academic Publishers, Boston. Manufactured in The Netherlands.
- Basis Construction from Power Series Expansions of Value Functions
- Learning Hierarchical Models of Activity Sarah Osentoski, Victoria Manfredi, Sridhar Mahadevan
- Multiscale Analysis of Document Corpora Based on Diffusion Models Chang Wang and Sridhar Mahadevan
- Representation Discovery in Sequential Decision Making Sridhar Mahadevan
- Learning Representation and Control In Continuous Markov Decision Processes Sridhar Mahadevan
- Learning Hierarchical Partially Observable Markov Decision Process Models for Robot Navigation
- Journal of Machine Learning Research 8 (2007) 2169-2231 Submitted 6/06; Revised 3/07; Published 9/07 Proto-value Functions: A Laplacian Framework for Learning
- Hierarchical Memory-Based Reinforcement Learning
- Coarticulation in Markov Decision Khashayar Rohanimanesh, Robert Platt, Sridhar Mahadevan, Roderic Grupen
- Optimality Criteria in Reinforcement Learning Sridhar Mahadevan \Lambda
- Multiscale Manifold Alignment Chang Wang and Sridhar Mahadevan
- Sparse Approximate Policy Evaluation using Graph-based Basis Functions
- Manifold Alignment without Correspondence Chang Wang and Sridhar Mahadevan
- Multiscale Analysis of Document Corpora Based on Diffusion Models Chang Wang and Sridhar Mahadevan
- Basis Function Construction for Hierarchical Reinforcement Sarah Osentoski sosentos@cs.umass.edu
- Adaptive Mesh Compression in 3D Computer Graphics using Multiscale Manifold Learning
- Learning State-Action Basis Functions for Hierarchical MDPs Sarah Osentoski SOSENTOS@CS.UMASS.EDU
- Fast Direct Policy Evaluation using Multiscale Analysis of Markov Diffusion Processes
- Value Function Approximation with Diffusion Wavelets and Laplacian Eigenfunctions
- Fast Direct Policy Evaluation using Multiscale Analysis of Markov Diffusion Processes
- A Variational Learning Algorithm for the Abstract Hidden Markov Model and Sridhar Mahadevan
- Learning to Communicate and Act in Cooperative Multiagent Systems using Hierarchical Reinforcement Learning
- Hierarchical Policy Gradient Algorithms Mohammad Ghavamzadeh mgh@cs.umass.edu
- Proto-value Functions: A Laplacian Framework for Learning Representation and Control in Markov Decision Processes
- The NSF Workshop on Reinforcement Learning: Summary and Observations 1
- Kalman Filters for Prediction and Tracking in an Adaptive Sensor Network
- Probabilistic Plan Recognition in Multiagent Systems Suchi Saria
