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/

Internet

https://ieeexplore.ieee.org/document/8698861/

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