Learning Transferable Cooperative Behavior in Multi-Agent Teams

By Akshat Agarwal, Sumit Kumar, Katia P. Sycara. 2019

157 citations recorded by the source corpus.

While multi-agent interactions can be naturally modeled as a graph, the environment has traditionally been considered as a black box. To better utilize the inherent structure of our environment, we propose to create a shared agent-entity graph, where agents and environmental entities form vertices, and edges exist between the vertices which can communicate with each other, allowing agents to selectively attend to different parts of the environment, while also introducing invariance to the number of agents or entities present in the system as well as permutation invariance. We present state-of-the-art results on coverage, formation and line control tasks for multi-agent teams in a fully decentralized execution framework.

Subfields: Artificial Intelligence, Computer Science Applications

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Citations in the corpus

From Few to More: Large-scale Dynamic Multiagent Curriculum Learning (2019, background): “Although some existing works consider CL in multiagent settings, the way they utilize CL is quite simple, which is not the focus of these works (Agarwal, Kumar, and Sycara 2019).”
AI-QMIX: Attention and Imagination for Dynamic Multi-Agent Reinforcement Learning (2020, background): “While existing lines of research have explored curriculum learning to scale from small to larger scenarios [1, 21, 36] or collaboration with agents not seen during training [31, 4, 14], to our knowledge, no other works have explored the setting of simultaneous learning across diverse scenarios.”
A survey of multi-agent deep reinforcement learning with communication (2022, unknown): “Existing Knowledge DIAL [73]; RIAL [73]; CommNet [48]; GCL [61]; BiCNet [50]; MS-MARL-GCM [88]; IC [62]; DGN [81]; TarMAC [89]; MAGNet-SA-GS-MG [40]; MADDPG-M [41]; IC3Net [49]; MD-MADDPG [42]; SchedNet [43]; DCC-MD [82]; Agent-Entity Graph [74]; VBC [31]; NDQ [75]; IMAC [66]; GA-Comm [83]; Gated-AC”
Graph Convolutional Reinforcement Learning (2018, unknown): “Agarwal et al. (2019) used attention mechanism for communication and proposed a curriculum learning for transferable cooperation.”
Randomized Entity-wise Factorization for Multi-Agent Reinforcement Learning (2020, background): “Several approaches devise attention or graph-neural-network based models for handling variable sized inputs and focus on learning curricula to progress on increasingly large/challenging settings (Long et al., 2020; Baker et al., 2019; Wang et al., 2020c; Agarwal et al., 2019).”