David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Ezio Di Nucci
Jack Alan Reynolds
Learn more about PhilPapers
Di erent from existing reinforcement learning algorithms that generate only reactive policies and existing probabilis tic planning algorithms that requires a substantial amount of a priori knowledge in order to plan we devise a two stage bottom up learning to plan process in which rst reinforce ment learning dynamic programming is applied without the use of a priori domain speci c knowledge to acquire a reactive policy and then explicit plans are extracted from the learned reactive policy Plan extraction is based on a beam search algorithm that performs temporal projection in a restricted fashion guided by the value functions re sulting from reinforcement learning dynamic programming Experiments and theoretical analysis are presented..
|Keywords||No keywords specified (fix it)|
|Categories||categorize this paper)|
Setup an account with your affiliations in order to access resources via your University's proxy server
Configure custom proxy (use this if your affiliation does not provide a proxy)
|Through your library||
References found in this work BETA
No references found.
Citations of this work BETA
No citations found.
Similar books and articles
Ron Sun (1997). Learning, Action, and Consciousness: A Hybrid Approach Toward Modeling Consciousness. Neural Networks 10:1317-33.
Ron Sun, Todd Peterson & Edward Merrill, Bottom-Up Skill Learning in Reactive Sequential Decision Tasks.
Edward Merrillb & Todd Petersonb (2001). From Implicit Skills to Explicit Knowledge: A Bottom‐Up Model of Skill Learning. Cognitive Science 25 (2):203-244.
Enrico Blanzieri (1997). Dynamical Learning Algorithms for Neural Networks and Neural Constructivism. Behavioral and Brain Sciences 20 (4):559-559.
Ron Sun, Beyond Simple Rule Extraction: The Extraction of Planning Knowledge From Reinforcement Learners.
Added to index2009-06-13
Total downloads4 ( #548,944 of 1,792,259 )
Recent downloads (6 months)1 ( #464,595 of 1,792,259 )
How can I increase my downloads?