IEICE TRANSACTIONS on Fundamentals
Learning in Two-Player Matrix Games by Policy Gradient Lagging Anchor
Summary :
It is known that policy gradient algorithm can not guarantee the convergence to a Nash equilibrium in mixed policies when it is applied in matrix games. To overcome this problem, we propose a novel multi-agent reinforcement learning (MARL) algorithm called a policy gradient lagging anchor (PGLA) algorithm. And we prove that the agents' policies can converge to a Nash equilibrium in mixed policies by using the PGLA algorithm in two-player two-action matrix games. By simulation, we confirm the convergence and also show that the PGLA algorithm has a better convergence than the LR-I lagging anchor algorithm.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E102-A No.4 pp.708-711
- Publication Date
- 2019/04/01
- Publicized
- Online ISSN
- 1745-1337
- DOI
- 10.1587/transfun.E102.A.708
- Type of Manuscript
- LETTER
- Category
- Mathematical Systems Science
Authors
Shiyao DING
Osaka University
Toshimitsu USHIO
Osaka University
Keyword
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Shiyao DING, Toshimitsu USHIO, "Learning in Two-Player Matrix Games by Policy Gradient Lagging Anchor" in IEICE TRANSACTIONS on Fundamentals,
vol. E102-A, no. 4, pp. 708-711, April 2019, doi: 10.1587/transfun.E102.A.708.
Abstract: It is known that policy gradient algorithm can not guarantee the convergence to a Nash equilibrium in mixed policies when it is applied in matrix games. To overcome this problem, we propose a novel multi-agent reinforcement learning (MARL) algorithm called a policy gradient lagging anchor (PGLA) algorithm. And we prove that the agents' policies can converge to a Nash equilibrium in mixed policies by using the PGLA algorithm in two-player two-action matrix games. By simulation, we confirm the convergence and also show that the PGLA algorithm has a better convergence than the LR-I lagging anchor algorithm.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E102.A.708/_p
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@ARTICLE{e102-a_4_708,
author={Shiyao DING, Toshimitsu USHIO, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Learning in Two-Player Matrix Games by Policy Gradient Lagging Anchor},
year={2019},
volume={E102-A},
number={4},
pages={708-711},
abstract={It is known that policy gradient algorithm can not guarantee the convergence to a Nash equilibrium in mixed policies when it is applied in matrix games. To overcome this problem, we propose a novel multi-agent reinforcement learning (MARL) algorithm called a policy gradient lagging anchor (PGLA) algorithm. And we prove that the agents' policies can converge to a Nash equilibrium in mixed policies by using the PGLA algorithm in two-player two-action matrix games. By simulation, we confirm the convergence and also show that the PGLA algorithm has a better convergence than the LR-I lagging anchor algorithm.},
keywords={},
doi={10.1587/transfun.E102.A.708},
ISSN={1745-1337},
month={April},}
Copy
TY - JOUR
TI - Learning in Two-Player Matrix Games by Policy Gradient Lagging Anchor
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 708
EP - 711
AU - Shiyao DING
AU - Toshimitsu USHIO
PY - 2019
DO - 10.1587/transfun.E102.A.708
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E102-A
IS - 4
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - April 2019
AB - It is known that policy gradient algorithm can not guarantee the convergence to a Nash equilibrium in mixed policies when it is applied in matrix games. To overcome this problem, we propose a novel multi-agent reinforcement learning (MARL) algorithm called a policy gradient lagging anchor (PGLA) algorithm. And we prove that the agents' policies can converge to a Nash equilibrium in mixed policies by using the PGLA algorithm in two-player two-action matrix games. By simulation, we confirm the convergence and also show that the PGLA algorithm has a better convergence than the LR-I lagging anchor algorithm.
ER -
English
