Retaining Suboptimal Actions to Follow Shifting Optima in Multi-Agent Reinforcement Learning
arXiv:2602.17062v2 Announce Type: replace Abstract: Value decomposition is a core approach for cooperative multi-agent reinforcement learning (MARL). However, existing methods still rely on a single optimal action and struggle to adapt when the underlying value function shifts during training, often converging to suboptimal policies. To address this limitation, we propose Successive Sub-value Q-learning (S2Q), which learns multiple sub-value functions to retain alternative high-value actions. Incorporating these sub-value functions into a Softmax-based behavior policy, S2Q encourages persisten
This research addresses a critical limitation in multi-agent reinforcement learning, a field rapidly evolving towards more adaptable and robust AI systems.
Improved MARL methods are crucial for developing more resilient and versatile AI agents capable of operating in dynamic and unpredictable environments, enhancing their real-world applicability.
The ability of MARL systems to adapt to shifting optima will reduce the need for constant human recalibration, making autonomous agents more robust and reliable.
- · AI agents developers
- · Robotics industry
- · Logistics and supply chain automation
- · Complex system management software
- · AI systems relying on static policies
- · Human operators performing continuous recalibration
More robust and adaptable autonomous systems become feasible across various industries.
Accelerates the development of sophisticated AI agents capable of handling real-world complexity without collapsing.
Potentially enables new forms of truly autonomous, self-optimizing organizational structures or distributed control systems.
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