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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Vydané v:	2020 International Conference on Innovation and Intelligence for Informatics, Computing and Technologies (3ICT) s. 1 - 6
Hlavný autor:	Farag, Wael
Médium:	Konferenčný príspevok..
Jazyk:	English
Vydavateľské údaje:	IEEE 20.12.2020
Predmet:	D4PG Games Informatics Machine Learning Machine learning algorithms Multi-Agent Policy-Gradients Methods Reinforcement learning Task analysis Technological innovation Training
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