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COLT
2006
Springer
15 years 8 months ago
Improving Random Projections Using Marginal Information
Abstract. We present an improved version of random projections that takes advantage of marginal norms. Using a maximum likelihood estimator (MLE), marginconstrained random projecti...
Ping Li, Trevor Hastie, Kenneth Ward Church
AIED
2009
Springer
15 years 11 months ago
Off-Task Behavior in Narrative-Centered Learning Environments
Recent years have seen increasing interest in narrative-centered learning environments. However, the same qualities that make them engaging can also introduce seductive details tha...
Jonathan P. Rowe, Scott W. McQuiggan, Jennifer L. ...
139
Voted
CIRA
2007
IEEE
148views Robotics» more  CIRA 2007»
15 years 11 months ago
Reinforcement Learning with a Supervisor for a Mobile Robot in a Real-world Environment
– This paper describes two experiments with supervised reinforcement learning (RL) on a real, mobile robot. Two types of experiments were preformed. One tests the robot’s relia...
Karla Conn, Richard Alan Peters II
135
Voted
ATAL
2003
Springer
15 years 9 months ago
Coordination in multiagent reinforcement learning: a Bayesian approach
Much emphasis in multiagent reinforcement learning (MARL) research is placed on ensuring that MARL algorithms (eventually) converge to desirable equilibria. As in standard reinfor...
Georgios Chalkiadakis, Craig Boutilier
IJCAI
2001
15 years 6 months ago
R-MAX - A General Polynomial Time Algorithm for Near-Optimal Reinforcement Learning
R-max is a very simple model-based reinforcement learning algorithm which can attain near-optimal average reward in polynomial time. In R-max, the agent always maintains a complet...
Ronen I. Brafman, Moshe Tennenholtz