Reinforcement Learning Trees
1996 (English)Report (Other academic)
Two new reinforcement learning algorithms are presented. Both use a binary tree to store simple local models in the leaf nodes and coarser global models towards the root. It is demonstrated that a meaningful partitioning into local models can only be accomplished in a fused space consisting of both input and output. The first algorithm uses a batch like statistic procedure to estimate the reward functions in the fused space. The second one uses channel coding to represent the output- and input vectors allowing a simple iterative algorithm based on competing subsystems. The behaviors of both algorithms are illustrated in a preliminary experiment.
Place, publisher, year, edition, pages
Linköping, Sweden: Linköping University, Department of Electrical Engineering , 1996. , 8 p.
LiTH-ISY-R, ISSN 1400-3902 ; 1828
Learning algorithms, Autonomous systems
Engineering and Technology
IdentifiersURN: urn:nbn:se:liu:diva-53411ISRN: LiTH-ISY-R-1828OAI: oai:DiVA.org:liu-53411DiVA: diva2:288282