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Multiagent Soft Q-Learning
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Policy gradient methods are often applied to reinforcement learning in continuous multiagent games. These methods perform local search in the joint-action space, and as we show, they are susceptable to a game-theoretic pathology known as relative overgeneralization. To resolve this issue, we propose Multiagent Soft Q-learning, which can be seen as the analogue of applying Q-learning to continuous controls. We compare our method to MADDPG, a state-of-the-art approach, and show that our method achieves better coordination in multiagent cooperative tasks, converging to better local optima in the joint action space.
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Mitigating Relative Over-Generalization in Multi-Agent Reinforcement Learning
MMQ mitigates relative over-generalization in decentralized MARL by taking the maximum over sampled next states predicted from quantile bounds.
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