Building Poker Agent Using Reinforcement Learning with Neural Networks

Annija Rupeneite

Abstract

Poker is a game with incomplete and imperfect information. The ability to estimate opponent and interpret its actions makes a player as a world class player. Finding optimal game strategy is not enough to win poker game. As in real life as in online poker game, the most time of it consists of opponent analysis. This paper illustrates a development of poker agent using reinforcement learning with neural networks.

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Paper Citation


in Harvard Style

Rupeneite A. (2014). Building Poker Agent Using Reinforcement Learning with Neural Networks . In Doctoral Consortium - DCINCO, (ICINCO 2014) ISBN , pages 22-29


in Bibtex Style

@conference{dcinco14,
author={Annija Rupeneite},
title={Building Poker Agent Using Reinforcement Learning with Neural Networks },
booktitle={Doctoral Consortium - DCINCO, (ICINCO 2014)},
year={2014},
pages={22-29},
publisher={SciTePress},
organization={INSTICC},
doi={},
isbn={},
}


in EndNote Style

TY - CONF
JO - Doctoral Consortium - DCINCO, (ICINCO 2014)
TI - Building Poker Agent Using Reinforcement Learning with Neural Networks
SN -
AU - Rupeneite A.
PY - 2014
SP - 22
EP - 29
DO -