Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Tasks
Georgios Papoudakis, Filippos Christianos, Lukas Schäfer, Stefano V. Albrecht
Abstract
Multi-agent deep reinforcement learning (MARL) suffers from a lack of commonly-used evaluation tasks and criteria, making comparisons between approaches difficult. In this work, we provide a systematic evaluation and comparison of three different classes of MARL algorithms (independent learning, centralised multi-agent policy gradient, value decomposition) in a diverse range of cooperative multi-agent learning tasks....
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