`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: Mahadevan:Sridhar

### Contributed to:

### 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.