ITEM METADATA RECORD
Title: Relational reinforcement learning
Authors: Dzeroski, Saso ×
De Raedt, Luc
Blockeel, Hendrik #
Issue Date: 1998
Conference: 4th International Workshop on Multi-Strategy Learning location:Desenzano Del Garda, Italy date:June 11-13, 1998
Abstract: elational reinforcement learning is presented,
a learning technique that combines reinforcement learning
with relational learning or inductive logic programming.
Due to the use of a more expressive representation language
to represent states, actions and Q-functions,
relational reinforcement learning can be potentially applied
to a new range of learning tasks.
One such task that we investigate is
planning in the blocks world, where
it is assumed that the effects of the actions
are unknown to the agent and the agent has to learn a policy.
Within this simple domain we show that relational reinforcement
learning solves some existing problems with
reinforcement learning. In particular,
relational reinforcement learning allows us to
employ structural representations, make abstraction
of specific goals pursued and exploit the results
of previous learning phases when addressing new (more complex) situations.
Description: pp. 64-64 in Proc. 4th Int. Workshop on Multi-Strategy Learning, 1998
Publication status: published
KU Leuven publication type: IMa
Appears in Collections:Informatics Section
× corresponding author
# (joint) last author

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