Retaining Suboptimal Actions to Follow Shifting Optima in Multi-Agent Reinforcement Learning
arXiv:2602.17062v1 Announce Type: new 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 persistent exploration and enables $Q^{\text{tot}}$ to adjust quickly to the changing optima. Experiments on challenging MARL benchmarks confirm that S2Q consistently outperforms various MARL algorithms, demonstrating improved adaptability and overall performance. Our code is available at https://github.com/hyeon1996/S2Q.
arXiv:2602.17062v1 Announce Type: new 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 persistent exploration and enables $Q^{\text{tot}}$ to adjust quickly to the changing optima. Experiments on challenging MARL benchmarks confirm that S2Q consistently outperforms various MARL algorithms, demonstrating improved adaptability and overall performance. Our code is available at https://github.com/hyeon1996/S2Q.
Executive Summary
The article proposes Successive Sub-value Q-learning (S2Q), a novel approach to cooperative multi-agent reinforcement learning (MARL) that learns multiple sub-value functions to retain alternative high-value actions. This enables the model to adapt quickly to changing optima and encourages persistent exploration. Experiments demonstrate that S2Q outperforms various MARL algorithms, showcasing improved adaptability and overall performance. The approach addresses the limitation of existing methods that rely on a single optimal action and struggle to adapt to shifting value functions during training.
Key Points
- ▸ Successive Sub-value Q-learning (S2Q) learns multiple sub-value functions
- ▸ S2Q encourages persistent exploration and adapts to changing optima
- ▸ Experiments demonstrate improved performance over various MARL algorithms
Merits
Improved Adaptability
S2Q's ability to learn multiple sub-value functions enables it to adapt quickly to changing optima, making it more effective in dynamic environments.
Demerits
Computational Complexity
Learning multiple sub-value functions may increase computational complexity, potentially limiting the applicability of S2Q in certain scenarios.
Expert Commentary
The proposed S2Q approach represents a significant advancement in cooperative MARL, addressing a long-standing limitation of existing methods. By learning multiple sub-value functions, S2Q enables more effective adaptation to changing environments, which is crucial in real-world applications. The experimental results demonstrate the efficacy of S2Q, and its potential impact on the development of more intelligent and autonomous systems. However, further research is needed to fully explore the implications of S2Q and its potential applications.
Recommendations
- ✓ Further investigation into the computational complexity of S2Q and its potential limitations
- ✓ Exploration of S2Q's applicability to various domains, including robotics and autonomous vehicles
