Rethinking the Implementation Tricks and Monotonicity Constraint in Cooperative Multi-Agent Reinforcement Learning. (arXiv:2102.03479v18 [cs.LG] UPDATED)

Many complex multi-agent systems such as robot swarms control and autonomous
vehicle coordination can be modeled as Multi-Agent Reinforcement Learning
(MARL) tasks. QMIX, a widely popular MARL algorithm, has been used as a
baseline for the benchmark environments, e.g., Starcraft Multi-Agent Challenge
(SMAC), Difficulty-Enhanced Predator-Prey (DEPP). Recent variants of QMIX
target relaxing the monotonicity constraint of QMIX, allowing for performance
improvement in SMAC. In this paper, we investigate the code-level optimizations
of these variants and the monotonicity constraint. (1) We …

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