Travelled to:1 × Finland
1 × Germany
1 × Israel
3 × USA
Collaborated with:D.J.Lizotte D.F.Wilkinson D.Szafron M.M.Veloso ∅ S.A.Murphy U.Syed R.E.Schapire A.Ghodsi M.Johanson N.Burch T.Wang D.Schuurmans M.Cutumisu R.S.Sutton P.McCracken M.James J.Neufeld
Talks about:learn (7) use (3) reinforc (2) converg (2) reward (2) action (2) sampl (2) rate (2) game (2) apprenticeship (1)
Person: Michael H. Bowling
DBLP: Bowling:Michael_H=
Contributed to:
201020082006200520012000
2008Wrote 9 papers:
- ICML-2010-LizotteBM #analysis #learning #multi #performance #random
- Efficient Reinforcement Learning with Multiple Reward Functions for Randomized Controlled Trial Analysis (DJL, MHB, SAM), pp. 695–702.
- ICML-2008-BowlingJBS #evaluation #game studies
- Strategy evaluation in extensive games with importance sampling (MHB, MJ, NB, DS), pp. 72–79.
- ICML-2008-SyedBS #learning #linear #programming #using
- Apprenticeship learning using linear programming (US, MHB, RES), pp. 1032–1039.
- ICML-2006-BowlingMJNW #learning #policy #predict #using
- Learning predictive state representations using non-blind policies (MHB, PM, MJ, JN, DFW), pp. 129–136.
- ICML-2005-BowlingGW
- Action respecting embedding (MHB, AG, DFW), pp. 65–72.
- ICML-2005-WangLBS #online #optimisation
- Bayesian sparse sampling for on-line reward optimization (TW, DJL, MHB, DS), pp. 956–963.
- ICML-2001-BowlingV #convergence #learning
- Convergence of Gradient Dynamics with a Variable Learning Rate (MHB, MMV), pp. 27–34.
- ICML-2000-Bowling #convergence #learning #multi #problem
- Convergence Problems of General-Sum Multiagent Reinforcement Learning (MHB), pp. 89–94.
- AIIDE-2008-CutumisuSBS #game studies #learning #using
- Agent Learning using Action-Dependent Learning Rates in Computer Role-Playing Games (MC, DS, MHB, RSS).