Multi-Agent Actor-Critic with Hierarchical Graph Attention Network
By Heechang Ryu, Hayong Shin, Jinkyoo Park. 2019
149 citations recorded by the source corpus.
Most previous studies on multi-agent reinforcement learning focus on deriving decentralized and cooperative policies to maximize a common reward and rarely consider the transferability of trained policies to new tasks. This prevents such policies from being applied to more complex multi-agent tasks. To resolve these limitations, we propose a model that conducts both representation learning for multiple agents using hierarchical graph attention network and policy learning using multi-agent actor-critic. The hierarchical graph attention network is specially designed to model the hierarchical relationships among multiple agents that either cooperate or compete with each other to derive more advanced strategic policies. Two attention networks, the inter-agent and inter-group attention layers, are used to effectively model individual and group level interactions, respectively. The two attention networks have been proven to facilitate the transfer of learned policies to new tasks with different agent compositions and allow one to interpret the learned strategies. Empirically, we demonstrate that the proposed model outperforms existing methods in several mixed cooperative and competitive tasks.
Subfields: Artificial Intelligence
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In Artificial Intelligence
- Filippos Christianos
- Georgios Papoudakis
- Stefano V. Albrecht
- Lukas Schäfer
- Arrasy Rahman
- Peter Stone
Citations in the corpus
- Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Tasks (2020, methodology): “These algorithm classes are frequently used in the literature either as baselines or building blocks for more complex algorithms [He et al., 2016, Sukhbaatar et al., 2016, Foerster et al., 2016, Raileanu et al., 2018, Jaques et al., 2019, Iqbal and Sha, 2019, Du et al., 2019, Ryu et al., 2020].”
- Comparative Evaluation of Multi-Agent Deep Reinforcement Learning Algorithms (2020, background): “These algorithm classes are frequently used in the literature either as baselines or building blocks for more complex algorithms [8, 11, 13, 15, 17, 27, 29, 32].”