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» Safe exploration for reinforcement learning
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ATAL
2010
Springer
13 years 8 months ago
PAC-MDP learning with knowledge-based admissible models
PAC-MDP algorithms approach the exploration-exploitation problem of reinforcement learning agents in an effective way which guarantees that with high probability, the algorithm pe...
Marek Grzes, Daniel Kudenko
HYBRID
2005
Springer
14 years 2 months ago
Learning Multi-modal Control Programs
Abstract. Multi-modal control is a commonly used design tool for breaking up complex control tasks into sequences of simpler tasks. In this paper, we show that by viewing the contr...
Tejas R. Mehta, Magnus Egerstedt
IJCAI
2007
13 years 10 months ago
Using Linear Programming for Bayesian Exploration in Markov Decision Processes
A key problem in reinforcement learning is finding a good balance between the need to explore the environment and the need to gain rewards by exploiting existing knowledge. Much ...
Pablo Samuel Castro, Doina Precup
KDD
2010
ACM
289views Data Mining» more  KDD 2010»
13 years 6 months ago
Exploitation and exploration in a performance based contextual advertising system
The dynamic marketplace in online advertising calls for ranking systems that are optimized to consistently promote and capitalize better performing ads. The streaming nature of on...
Wei Li 0010, Xuerui Wang, Ruofei Zhang, Ying Cui, ...
EWCBR
2008
Springer
13 years 10 months ago
Forgetting Reinforced Cases
To meet time constraints, a CBR system must control the time spent searching in the case base for a solution. In this paper, we presents the results of a case study comparing the p...
Houcine Romdhane, Luc Lamontagne