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» Reinforcement learning in a nutshell
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COGSR
2011
71views more  COGSR 2011»
13 years 3 months ago
Psychological models of human and optimal performance in bandit problems
In bandit problems, a decision-maker must choose between a set of alternatives, each of which has a fixed but unknown rate of reward, to maximize their total number of rewards ov...
Michael D. Lee, Shunan Zhang, Miles Munro, Mark St...
JAIR
2011
187views more  JAIR 2011»
13 years 3 months ago
A Monte-Carlo AIXI Approximation
This paper describes a computationally feasible approximation to the AIXI agent, a universal reinforcement learning agent for arbitrary environments. AIXI is scaled down in two ke...
Joel Veness, Kee Siong Ng, Marcus Hutter, William ...
JCST
2010
109views more  JCST 2010»
13 years 3 months ago
The Inverse Classification Problem
In this paper, we examine an emerging variation of the classification problem, which is known as the inverse classification problem. In this problem, we determine the features to b...
Charu C. Aggarwal, Chen Chen, Jiawei Han
AGI
2011
13 years 12 days ago
Comparing Humans and AI Agents
Comparing humans and machines is one important source of information about both machine and human strengths and limitations. Most of these comparisons and competitions are performe...
Javier Insa-Cabrera, David L. Dowe, Sergio Espa&nt...
CIMCA
2008
IEEE
14 years 3 months ago
Tree Exploration for Bayesian RL Exploration
Research in reinforcement learning has produced algorithms for optimal decision making under uncertainty that fall within two main types. The first employs a Bayesian framework, ...
Christos Dimitrakakis