Scalable Deep Multi-Agent Reinforcement Learning via Observation Embedding and Parameter Noise

Scalable Deep Multi-Agent Reinforcement Learning via Observation Embedding and Parameter Noise

In this paper, we explore a scalable deep reinforcement learning (DRL) method for environments with multi-agents. Due to the explosive increase of the input dimensionality with the number of agents, most existing DRL methods are only able to cope with single-agent settings, or for only a small numbe...

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Bibliographic Details
Main Authors:	Jian Zhang, Yaozong Pan, Haitao Yang, Yuqiang Fang
Format:	Article
Language:	English
Published:	IEEE 2019-01-01
Series:	IEEE Access
Subjects:	Artificial intelligence multi-agent deep reinforcement learning deep deterministic policy gradient actor-critic centralized training with decentralized execution
Online Access:	https://ieeexplore.ieee.org/document/8698861/

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