Markov games as a framework for multi-agent reinforcement learning

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Markov games as a framework for multi-agent reinforcement learning (1994)

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by Michael L. Littman

Venue:	IN PROCEEDINGS OF THE ELEVENTH INTERNATIONAL CONFERENCE ON MACHINE LEARNING
Citations:	601 - 13 self

BibTeX

@INPROCEEDINGS{Littman94markovgames,
    author = {Michael L. Littman},
    title = {Markov games as a framework for multi-agent reinforcement learning},
    booktitle = {IN PROCEEDINGS OF THE ELEVENTH INTERNATIONAL CONFERENCE ON MACHINE LEARNING},
    year = {1994},
    pages = {157--163},
    publisher = {Morgan Kaufmann}
}

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Abstract

In the Markov decision process (MDP) formalization of reinforcement learning, a single adaptive agent interacts with an environment defined by a probabilistic transition function. In this solipsistic view, secondary agents can only be part of the environment and are therefore fixed in their behavior. The framework of Markov games allows us to widen this view to include multiple adaptive agents with interacting or competing goals. This paper considers a step in this direction in which exactly two agents with diametrically opposed goals share an environment. It describes a Q-learning-like algorithm for finding optimal policies and demonstrates its application to a simple two-player game in which the optimal policy is probabilistic.

Keyphrases

markov game multi-agent reinforcement learning optimal policy simple two-player game multiple adaptive agent q-learning-like algorithm single adaptive agent interacts probabilistic transition function reinforcement learning secondary agent goal share solipsistic view markov decision process

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