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arXiv · 2002.06684

R-MADDPG for Partially Observable Environments and Limited Communication

Rose E. Wang·Michael Everett·Jonathan P. How
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Abstract

There are several real-world tasks that would benefit from applying multiagent reinforcement learning (MARL) algorithms, including the coordination among self-driving cars. The real world has challenging conditions for multiagent learning systems, such as its partial observable and nonstationary nature. Moreover, if agents must share a limited resource (e.g. network bandwidth) they must all learn how to coordinate resource use. This paper introduces a deep recurrent multiagent actor-critic framework (R-MADDPG) for handling multiagent coordination under partial observable set-tings and limited communication. We investigate recurrency effects on performance and communication use of a team of agents. We demonstrate that the resulting framework learns time dependencies for sharing missing observations, handling resource limitations, and developing different communication patterns among agents.

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Rose E. Wang, Michael Everett, Jonathan P. How. 2020-02-16. R-MADDPG for Partially Observable Environments and Limited Communication. https://arxiv.org/abs/2002.06684

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