Competitive Policy Optimization
Abstract
A core challenge in policy optimization in competitive Markov decision processes is the design of efficient optimization methods with desirable convergence and stability properties. To tackle this, we propose competitive policy optimization (CoPO), a novel policy gradient approach that exploits the game-theoretic nature of competitive games to derive policy updates. Motivated by the competitive gradient optimization method, we derive a bilinear approximation of the game objective. In contrast, off-the-shelf policy gradient methods utilize only linear approximations, and hence do not capture interactions among the players. We instantiate CoPO in two ways:(i) competitive policy gradient, and (ii) trust-region competitive policy optimization. We theoretically study these methods, and empirically investigate their behavior on a set of comprehensive, yet challenging, competitive games. We observe that they provide stable optimization, convergence to sophisticated strategies, and higher scores when played against baseline policy gradient methods.
Cite
@article{arxiv.2006.10611,
title = {Competitive Policy Optimization},
author = {Manish Prajapat and Kamyar Azizzadenesheli and Alexander Liniger and Yisong Yue and Anima Anandkumar},
journal= {arXiv preprint arXiv:2006.10611},
year = {2020}
}
Comments
11 pages main paper, 6 pages references, and 31 pages appendix. 14 figures