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PKDD
2009
Springer
181views Data Mining» more  PKDD 2009»
15 years 4 months ago
Active Learning for Reward Estimation in Inverse Reinforcement Learning
Abstract. Inverse reinforcement learning addresses the general problem of recovering a reward function from samples of a policy provided by an expert/demonstrator. In this paper, w...
Manuel Lopes, Francisco S. Melo, Luis Montesano
CORR
2006
Springer
140views Education» more  CORR 2006»
14 years 9 months ago
Nearly optimal exploration-exploitation decision thresholds
While in general trading off exploration and exploitation in reinforcement learning is hard, under some formulations relatively simple solutions exist. Optimal decision thresholds ...
Christos Dimitrakakis
ICML
1998
IEEE
15 years 10 months ago
The MAXQ Method for Hierarchical Reinforcement Learning
This paper presents a new approach to hierarchical reinforcement learning based on the MAXQ decomposition of the value function. The MAXQ decomposition has both a procedural seman...
Thomas G. Dietterich
ECAI
2000
Springer
15 years 2 months ago
Learning to Use Operational Advice
We address the problem of advice-taking in a given domain, in particular for building a game-playing program. Our approach to solving it strives for the application of machine lea...
Johannes Fürnkranz, Bernhard Pfahringer, Herm...
CIG
2005
IEEE
14 years 11 months ago
A Survey on Multiagent Reinforcement Learning Towards Multi-Robot Systems
Abstract- Multiagent reinforcement learning for multirobot systems is a challenging issue in both robotics and artificial intelligence. With the ever increasing interests in theor...
Erfu Yang, Dongbing Gu