We provide two Q-Learning based systems that follow the independent learners and centralised training with decentralised execution paradigms:

• rec-IQL
• rec-QMIX

rec-IQL is a multi-agent version of DQN that uses double DQN and has a GRU memory module and rec-QMIX is an implementation of QMIX in JAX that uses monontic value function decomposition.

• Playing Atari with Deep Reinforcement Learning
• Multiagent Cooperation and Competition with Deep Reinforcement Learning
• QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning