Yonghyeon Jo, Sunwoo Lee, Seungyul Han
The paper introduces Successive Sub-value Q-learning (S2Q), a method that improves adaptability in multi-agent reinforcement learning by retaining multiple high-value actions, outperforming existing algorithms.
In multi-agent reinforcement learning, agents often need to work together to learn the best actions to take. Traditional methods focus on finding a single 'best' action, but this can be limiting when the environment changes. This research presents a new approach called Successive Sub-value Q-learning (S2Q), which keeps track of several good actions instead of just one. By doing this, the agents can adapt more quickly when conditions change, leading to better performance in various tests. The researchers have shared their code publicly for others to use and build upon.
