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Influence-Based Abstraction in Deep Reinforcement Learning

Miguel Suau de Castro, Elena Congeduti, Rolf A.N. Starre, Aleksander Czechowski, and Frans A. Oliehoek. Influence-Based Abstraction in Deep Reinforcement Learning. In Proceedings of the AAMAS Workshop on Adaptive Learning Agents (ALA), May 2019.

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Abstract

Real-world systems are typically extremely complex, consisting of thousands, oreven millions of state variables. Unfortunately, applying reinforcementlearning algorithms to handle complex tasks becomes more and morechallenging as the number of state variables increases. In this paper, webuild on the concept of <i>influence-based abstraction</i> which tries to tacklesuch scalability issues by decomposing large systems into small regions. We explore this method in the context of deep reinforcement learning, showing that by keeping track of a small set of variables in the history of previous actions and observations we can learn policies that can effectively control a local region in the global system.

BibTeX Entry

@inproceedings{Suau19ALA,
    title =     {Influence-Based Abstraction in Deep Reinforcement Learning},
    author =    {Miguel Suau de Castro and
                 Elena Congeduti and
                 Rolf A.N. Starre and 
                 Aleksander Czechowski and
                 Frans A. Oliehoek},
    booktitle = ALA19,
    year =      2019,
    month =     may,
    OPTurl =    TBA,
    keywords =  {refereed, workshop},
    abstract={
Real-world systems are typically extremely complex, consisting of thousands, or
even millions of state variables.  Unfortunately, applying reinforcement
learning algorithms to handle complex tasks becomes more and more
challenging as the number of state variables increases. In this paper,  we
build on the concept of \emph{influence-based abstraction} which tries to tackle
such scalability issues by decomposing large systems into small regions. We explore this method in the context of deep reinforcement learning, showing that by keeping track of a small set of variables in the history of previous actions and observations we can learn policies that can effectively control a local region in the global system.
    }
}

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