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Travelled to:
1 × Australia
1 × Finland
1 × Germany
1 × Italy
1 × Slovenia
1 × United Kingdom
8 × USA
Collaborated with:
M.Ghavamzadeh C.Wang J.Johns S.Osentoski M.Maggioni K.Rohanimanesh G.Wang J.Connell P.Tadepalli
Talks about:
learn (12) reinforc (7) hierarch (5) function (4) discount (4) use (4) process (3) polici (3) markov (3) optim (3)

Person: Sridhar Mahadevan

DBLP DBLP: Mahadevan:Sridhar

Contributed to:

ICML 20082008
ICML 20072007
ICML 20062006
ICML 20052005
ICML 20032003
ICML 20022002
ICML 20012001
ICML 19991999
ICML 19961996
ICML 19941994
ML 19921992
ML 19911991
ML 19891989
ML 19881988

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.

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