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Reinforcement Learning and
Artificial
Intelligence (RLAI)
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Reinforcement
Learning and Artificial Intelligence
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edited by Rich Sutton
The ambition of this page
is to provide
top-level links into research, worldwide,
at the intersection of reinforcement
learning and artificial intelligence. You are encouraged to add
links to your own relevant
research.
RLAI research is research
directed toward the long-standing goals of AI
(understanding the mind, reproducing human abilities) and
is based on reinforcement learning ideas (learning from and while
interacting with the world). RLAI research does not include all that is
currently thought of as AI research, only
that which addresses problems or issues that people regularly encounter
in their
everyday lives. Similarly, RLAI research does not include research that
uses RL methods to solve problems that people do not face and excel
at. There is a delimited and fruitful area
of research at the confluence of the most ambitious goals of AI and the
solution
ideas that are arising from RL research.
- Subscribe to the Reinforcement
Learning Mailing List (RL-List)
- Check out RL-community.org
- Research
- Software Tools
- RL-Glue
- a standard for connecting RL agents and
environments
- RL-Library
- a repository of standard RL software, based on RL-Glue
- RL
Toolkit - the
tools below plus RL demos (python)
- Tiles
- tile coding
or
CMACs for function approximation
- G - 2D
graphics routines useable
across
platforms and languages
- Quickgraph
- for making quick
2D and 3D graphs
- RLAI People and Research Groups on the Web (this list is from
2005)
- Workshops and Conferences
- Should
artificially intelligent robots have the same rights as people?
- Inactive pages: Content,
RLGO, dimensions,
RLinterface 5/6/7, UofA software, programming
style, RLBB, Rich's
Principles, Rich's
Topics, Employment
opportunities, paper
bank,,