Retaining Suboptimal Actions to Follow Shifting Optima in Multi-Agent Reinforcement Learning

Ali NematiFeb 2022 sec read16 views

Researchers propose Successive Sub-value Q-learning (S2Q) to enhance multi-agent reinforcement learning by retaining multiple suboptimal actions, allowing for better adaptation to shifting value functions during training. This approach improves adaptability and performance in cooperative MARL scenarios, encouraging continuous exploration and quicker adjustment to changing conditions.

Read the full article at arXiv cs.AI (Artificial Intelligence)

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Retaining Suboptimal Actions to Follow Shifting Optima in Multi-Agent Reinforcement Learning

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