Reinforcement Learning workshop to follow ML93 -- Call for participation

[Rich Sutton]

                      Call for Participation

	"REINFORCEMENT LEARNING: What We Know, What We Need"

              an Informal Workshop to follow ML93 
       June 30 & July 1, University of Massachusetts, Amherst

Reinforcement learning is a simple way of framing the problem of an
autonomous agent learning and interacting with the world to achieve a
goal.  This has been an active area of machine learning research for the
last 5 years.  The objective of this workshop is to present concisely 
the current state of the art in reinforcement learning and to
identify and highlight critical open problems.

The intended audience is all learning researchers interested in
reinforcement learning; little prior knowledge of the area will be
assumed.  The first half of the workshop will consist mainly of
tutorial presentations, and the second half will define and explore
outstanding problems.  The entire workshop will last approximately one 
and a half days. Attendance will be open to all those registered for 
the main part of ML93 (June 27-29).  

Program Committee: Rich Sutton (chair), Nils Nilsson, Leslie
Kaelbling, Satinder Singh, Sridhar Mahadevan, Andy Barto, Steve
Whitehead

CALL FOR PAPERS.  Papers are solicited that lay out relevant
problem areas, i.e., for the second half of the workshop.  Proposals
are also solicited for polished tutorial presentations on basic 
topics of reinforcement learning for the first portion of the workshop.
The program has yet to be established, but will probably look something 
like the following (all names are provisional):

Session 1: Introduction

  The Challenge of Reinforcement Learning, by Rich Sutton
  History of RL, by Harry Klopf
  Q-learning
  Planning and Action Models, by Long-Ji Lin

Session 2: Theory

  Dynamic Programming, by Andy Barto
  Convergence of Q-learning and TD(lambda), by Peter Dayan

Session 3: Applications

  TD-Gammon, by Gerry Tesauro
  Robotics, by Sridhar Mahadevan

Session 4: Extensions

  Prioritized Sweeping, by Andrew Moore
  Eligibility Traces, by Rich Sutton

Sessions 5 & 6: Open Problems

  Generalization 
  Hidden State (short-term memory)
  Hierarchical RL
  Search Control
  Incorporating Prior Knowledge
  Exploration
  ...

If you are interested in attending the RL workshop, please register by 
sending a note with your name, email and physical addresses, level of 
interest, and a brief description of your current level of knowledge
about reinforcement learning, to:

   sutton at gte.com   
OR  
   Rich Sutton
   GTE Labs, MS-44 
   40 Sylvan Road 
   Waltham, MA  02254