CFP: Reinforcement learning in non-stationary environments workshop
- From: ville.kononen@xxxxxx (Ville K?n?nen)
- Date: 25 May 2005 04:20:36 -0700
--- CALL FOR PAPERS ---
The workshop on
REINFORCEMENT LEARNING IN NON-STATIONARY ENVIRONMENTS
in conjunction with the 16th European Conference on Machine Learning (ECML)
and the 9th European Conference on Principles and Practice of Knowledge
Discovery in Databases (PKDD)
Porto, Portugal, October 3-7, 2005
Homepage of the workshop:
http://www.cis.hut.fi/ECML2005/
Introduction
------------
Reinforcement Learning (RL) has attained a lot of attention in recent
years. This learning paradigm has now been established as a practical
tool for modeling autonomous learning agents. The most currently used
RL methods are based on Markov Decision Processes (MDPs) and RL itself
provides an efficient tool for solving MDPs.
One main assumption behind MDPs is that the environment obeys the Markov
property, i.e. state transitions are based only on the current state of
the environment and the actions selected by the learning agent. In many
problem domains this property is not fully satisfied. For example, in many
real problem instances, the learning agent is not capable to sense the real
state of the environment and thus the Markov property might no longer be
satisfied.
The problem becomes very apparent in systems where multiple RL agents are
active in the same environment. In these systems, state transitions depend
generally on the action selections of all agents in the system. If agents
can not fully observe the behavior of the others, the Markov property is
no longer satisfied, and the environment as experienced by a single agent
is non-stationary.
Aim
---
Quite some researchers coming from different backgrounds (multiagent
learning, distributed learning, parallel learning, swarm intelligence,
learning automata, etc.) have made interesting contributions to RL in
non-stationary environments. The aim of the workshop is to bring
together researchers from different backgrounds working on this topic,
in order to discuss the commonalities and the differences, and how
forces can be joint.
Appropriate topics for papers include, but are not limited to,
the following:
* Methods for handling non-stationarity in
- Multiagent RL
- POMDPs
- Swarm intelligence systems
- (Interconnected) Learning automata
* Function approximation in RL
* Game theory in RL
* Practical applications of RL in non-stationary environments
Related tutorial
---------------------
The related tutorial "Learning Automata as a Basis for Multiagent
Reinforcement Learning" will be arranged on October 3, 2005.
Key dates
---------
Paper submission deadline: July 25, 2005
Notification of acceptance: August 15, 2005
Final copy due: September 5, 2005
Workshop: October 7, 2005
Submissions
-----------
Please send your submissions by email in the PDF-format to:
ville.kononen@xxxxxx
Paper formatting guidelines can be found in the homepage of the workshop:
http://www.cis.hut.fi/ECML2005/
Program committee
-----------------
Michael Bowling University of Alberta
Michael Littman Rutgers University
Ann Nowé Vrije Universiteit Brussel
Timo Honkela Helsinki University of Technology
Ron Sun Rensselaer Polytechnic Institute
Ville Könönen Helsinki University of Technology
Donald C. II Wunsch University Missouri-Rolla
Kary Främling Helsinki University of Technology
Katja Verbeeck Vrije Universiteit Brussel
Tom Lenaerts Université Libre de Bruxelles
Olivier Sigaud 'AnimatLab', Laboratoire d'Informatique
de Paris 6 (Lip6)
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