Connectionists: Deadline extension for special issue in Neurocomputing: Multiobjective Reinforcement Learning: Theory and Applications

[Wiering, M.A.]

*Special Issue in the journal Neurocomputing:*

*Multiobjective Reinforcement Learning: Theory and Applications*

*** One month extension for submitting papers ***

There will be a month’s extension to the deadline for submitting papers for
the upcoming special issue of the Neurocomputing journal on the topic
“Multiobjective Reinforcement Learning: Theory and Applications”.

The full Call For Papers is included below. The submission website for
the journal
is located at: http://ees.elsevier.com/
<http://ees.elsevier.com/neucom/default.asp>

To ensure that all manuscripts are correctly identified for inclusion into
the special issue, it is important that authors select *SI:Multiobjective
RL *when they reach the “*Article Type” *step in the submission process.
------------------------------

*Multiobjective Reinforcement Learning: Theory and Applications*

Many real-life problems involve dealing with multiple objectives. For
example, in network routing the criteria consist of energy consumption,
latency, and channel capacity, which are in essence conflicting objectives.
When system designers want to optimize more than one objective, it is not
always clear a priori which objectives are correlated and how they
influence each other upon inspecting the problem at hand. As sometimes
objectives are conflicting, there usually exists no single optimal
solution. In those cases, it is desirable to obtain a set of trade-off
solutions between the objectives.

This problem has in the last decade also gained the attention of many
researchers in the field of Reinforcement Learning (RL). RL addresses
sequential decision problems in initially (possibly) unknown stochastic
environments. The goal is the maximization of the agent's reward in a
potentially unknown environment that is not always completely observable.
Until now, there has been no special issue in a journal or a book on
reinforcement learning that covered the topic of multiobjective
reinforcement learning.

*State of the art*

We consider the extension of RL to multiobjective (stochastic) rewards
(also called utilities in decision theory). Techniques from multi-objective
optimization are often used for multi-objective RL in order to improve the
exploration-exploitation tradeoff. Multi-objective optimization (MOO),
which is a sub-area of multi-criteria decision making (MCDM), considers the
optimization of more than one objective simultaneously and a decision maker
decides either which solutions are important for the user or when to
present these solutions to the user for further consideration. Currently,
MOO algorithms are seldom used for stochastic optimization, which makes it
an unexplored but very promising research area. The resulting algorithms
are a hybrid between MCDM and stochastic optimization. The RL algorithms
are enriched with the intuition and computational efficiency of MOO in
handing multi-objective problems.

*Aim and scope*

The main goal of this special issue is to solicit research on
multi-objective reinforcement learning. We encourage submissions

   -

   describing applications of MO methods in RL with a focus on optimization
   in difficult environments that are possibly dynamic, uncertain and
   partially observable.
   -

   offering theoretical insights in online or offline learning approaches
   for multi-objective problem domains.

*Topics of interests*

We enthusiastically solicit papers on relevant topics such as:

   -

   Reinforcement learning algorithms for solving multi-objective sequential
   decision making problems
   -

   Dynamic programming techniques and adaptive dynamic programming
   techniques handling multiple objectives
   -

   Theoretical results on the learnability of optimal policies, convergence
   of algorithms in qualitative settings, etc.
   -

   Decision making in dynamic and uncertain multi-objective environments
   -

   Applications and benchmark problems for multi-objective reinforcement
   learning.
   -

   Novel frameworks for multi-objective reinforcement learning
   -

   Real-world applications in engineering, business, computer science,
   biological sciences, scientific computation, etc. in dynamic and uncertain
   environments solved with multi-objective reinforcement learning

*Important dates*

   -

   Submissions open: December 1st 2015
   -

   Submissions close: March 5th 2016
   -

   Notification of acceptance: May 15th 2016
   -

   Final manuscript due: 1 August 2016
   -

   Expected publication date (online): November 2016
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