Workshop on Reinforcement Learning at UMass/Amherst 4/23

[Rich Sutton]

Dear Connectionists:

FYI, there will be a workshop on reinforcement learning at the
University of Massachusetts this friday, to which the public is welcome.
Complete information on the schedule, travel information, etc., is available
at http://www-anw.cs.umass.edu/nessrl.99.  A crude version of the current
schedule (it's better on the web) is given below.  Hope to see you there.

Rich Sutton


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                                      Current Schedule
  Time
                     Speaker
                                                            Title/Abstract
 8:30am
         Breakfast (bagels, coffee, etc)
 9:00am
         Amy McGovern
                                          Welcome
 9:05
         Invited Speaker: Manuela Veloso
         (CMU)
                                          What to Do Next?: Action
Selection in Dynamic
                                          Multi-Agent Environments
 10:00
         Mike Bowling
                                          A Parallel between Multi-agent
Reinforcement Learning
                                          and Stochastic Game Theory
         Peter Stone and Manuela Veloso
         (presented by Manuela Veloso)
                                          Team-Partitioned
Opaque-Transition Reinforcement
                                          Learning
         Will Uther
                                          Structural Generalization for
Growing Decision Forests
 11:10
                                               Break
 11:20
         Dan Bernstein
                                          Reusing Old Policies to
Accelerate Learning on New MDPs
         Bryan Singer
                                          Learning State Features from
Policies to Bias Exploration
                                          in Reinforcement Learning
         Theodore Perkins and Doina Precup
                                          Using Options for Knowledge
Transfer in Reinforcement
                                          Learning
 12:30
                                               Lunch
 2:00
         Michael Kearns
                                          Sparse Sampling Methods for
Learning and Planning in
                                          Large POMDPs
         Satinder Singh
                                          Approximate Planning for Factored
POMDPs using Belief
                                          State Simplification
         Rich Sutton
                                          Function Approximation in
Reinforcement Learning
         Yishay Mansour
                                          Finding a near best strategy from
a restricted class of
                                          strategies
 3:10
         Nicolas Meuleau
                                          Learning finite-state controllers
for partially-observable
                                          environments
         Keith Rogers
                                          Learning using the G-function
 3:55
                                               Break
 4:05
         Doina Precup
                                          Eligibility Traces for Off-Policy
Policy Evaluation
         Tom Kalt
                                          An RL approach to statistical
natural language parsing
 4:50
         Andrew Barto
                                          Closing remarks