Co-Evolving Multi-Agent Transfer Reinforcement Learning via Scenario Independent Representation

Co-Evolving Multi-Agent Transfer Reinforcement Learning via Scenario Independent Representation

Multi-Agent Reinforcement Learning (MARL) is extensively utilized for addressing intricate tasks that involve cooperation and competition among agents in Multi-Agent Systems (MAS). However, learning such tasks from scratch is challenging and often unfeasible, especially for MASs with a large number...

Full description

Bibliographic Details
Main Authors: Ayesha Siddiqua, Siming Liu, Ayesha Siddika Nipu, Anthony Harris, Yan Liu
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Access
Subjects:
Deep reinforcement learning
multi-agent system
transfer learning
curriculum learning
co-evolutionary multi-agent reinforcement learning
StarCraft II
Online Access:https://ieeexplore.ieee.org/document/10600472/

Internet

https://ieeexplore.ieee.org/document/10600472/

Similar Items