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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Bibliographic Details
Published in:2020 International Conference on Innovation and Intelligence for Informatics, Computing and Technologies (3ICT) pp. 1 - 6
Main Author: Farag, Wael
Format: Conference Proceeding
Language:English
Published: IEEE 20.12.2020
Subjects:
D4PG
Games
Informatics
Machine Learning
Machine learning algorithms
Multi-Agent
Policy-Gradients Methods
Reinforcement learning
Task analysis
Technological innovation
Training
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Summary: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 architectures of the actor and cretic networks are meticulously designed and the D4PG hyperparameters are carefully tuned. The trained agents are successfully tested in the Unity Machine Learning Agents environment. The testing shows the powerful performance of the D4PG algorithm in training multi-agents in complex environments.
DOI:10.1109/3ICT51146.2020.9311945