Multi-Agent Reinforcement Learning using the Deep Distributed Distributional Deterministic Policy Gradients Algorithm

In this paper, the Deep Distributed Distributional Deterministic Policy Gradients (D4PG) reinforcement learning algorithm is adopted to train a multi-agent action in a cooperative game environment. The algorithm is experimented on training the agents to play a game of tennis against each other. The...

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Veröffentlicht in:	2020 International Conference on Innovation and Intelligence for Informatics, Computing and Technologies (3ICT) S. 1 - 6
1. Verfasser:	Farag, Wael
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	IEEE 20.12.2020
Schlagworte:	D4PG Games Informatics Machine Learning Machine learning algorithms Multi-Agent Policy-Gradients Methods Reinforcement learning Task analysis Technological innovation Training
Online-Zugang:	Volltext
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