Person: Sridhar Mahadevan

DBLP: Mahadevan:Sridhar

Contributed to:

2008

2007

2006

2005

2003

2002

2001

1999

1996

1994

1992

1991

1989

1988

Wrote 17 papers:

ICML-2008-WangM #analysis #using

Manifold alignment using Procrustes analysis (CW, SM), pp. 1120–1127.

ICML-2007-JohnsM #approximate #graph

Constructing basis functions from directed graphs for value function approximation (JJ, SM), pp. 385–392.

ICML-2007-Mahadevan #3d #adaptation #learning #multi #using

Adaptive mesh compression in 3D computer graphics using multiscale manifold learning (SM), pp. 585–592.

ICML-2007-OsentoskiM #learning

Learning state-action basis functions for hierarchical MDPs (SO, SM), pp. 705–712.

ICML-2006-MaggioniM #analysis #evaluation #markov #multi #performance #policy #process #using

Fast direct policy evaluation using multiscale analysis of Markov diffusion processes (MM, SM), pp. 601–608.

ICML-2005-Mahadevan #learning

Proto-value functions: developmental reinforcement learning (SM), pp. 553–560.

ICML-2005-RohanimaneshM #approach #concurrent #generative #markov #named #process

Coarticulation: an approach for generating concurrent plans in Markov decision processes (KR, SM), pp. 720–727.

ICML-2003-GhavamzadehM #algorithm #policy

Hierarchical Policy Gradient Algorithms (MG, SM), pp. 226–233.

ICML-2002-GhavamzadehM #learning

Hierarchically Optimal Average Reward Reinforcement Learning (MG, SM), pp. 195–202.

ICML-2001-GhavamzadehM #learning

Continuous-Time Hierarchical Reinforcement Learning (MG, SM), pp. 186–193.

ICML-1999-WangM #markov #optimisation #process

Hierarchical Optimization of Policy-Coupled Semi-Markov Decision Processes (GW, SM), pp. 464–473.

ICML-1996-Mahadevan #learning

Sensitive Discount Optimality: Unifying Discounted and Average Reward Reinforcement Learning (SM), pp. 328–336.

ICML-1994-Mahadevan #case study #learning

To Discount or Not to Discount in Reinforcement Learning: A Case Study Comparing R Learning and Q Learning (SM), pp. 164–172.

ML-1992-Mahadevan #learning #modelling #probability

Enhancing Transfer in Reinforcement Learning by Building Stochastic Models of Robot Actions (SM), pp. 290–299.

ML-1991-MahadevanC #architecture #learning #scalability

Scaling Reinforcement Learning to Robotics by Exploiting the Subsumption Architecture (SM, JC), pp. 328–332.

ML-1989-Mahadevan #problem #using

Using Determinations in EBL: A Solution to the incomplete Theory Problem (SM), pp. 320–325.

ML-1988-MahadevanT #learning #on the

On the Tractability of Learning from Incomplete Theories (SM, PT), pp. 235–241.