Learning to Cooperate via Policy Search
<|reference_start|>Learning to Cooperate via Policy Search: Cooperative games are those in which both agents share the same payoff structure. Value-based reinforcement-learning algorithms, such as variants of Q-learning, have been applied to learning cooperative games, but they only apply when the game state is completely observable to both agents. Policy search methods are a reasonable alternative to value-based methods for partially observable environments. In this paper, we provide a gradient-based distributed policy-search method for cooperative games and compare the notion of local optimum to that of Nash equilibrium. We demonstrate the effectiveness of this method experimentally in a small, partially observable simulated soccer domain.<|reference_end|>
@article{peshkin2001learning,
    title={Learning to Cooperate via Policy Search},
    author={Leonid Peshkin, Kee-Eung Kim, Nicolas Meuleau and Leslie Pack
  Kaelbling},
    journal={Sixteenth Conference on Uncertainty in Artificial Intelligence,
  2000},
    year={2001},
    archivePrefix={arXiv},
    eprint={cs/0105032},
    primaryClass={cs.LG cs.MA}
}