通过识别合作行为提升多智能体协作与竞争环境下的表现
An Improved Multi-Agent Algorithm for Cooperative and Competitive Environments by Identifying and Encouraging Cooperation among Agents
- 在MADDPG基础上引入合作奖励参数，增强协同行为
- 在PettingZoo环境中实现团队与个体奖励双提升
- 适合研究多智能体协作或博弈的算法开发者
我们提出一种改进的多智能体算法，通过识别并鼓励智能体间的合作行为来应对多智能体强化学习问题。首先分析现有算法在多智能体场景中的不足；随后，在已有算法MADDPG的基础上，引入新参数以提高当检测到合作行为时智能体获得的奖励。最后，在PettingZoo环境中的实验表明，该算法显著提升了团队总奖励与单个智能体的奖励水平。
原文摘要 · Abstract (English)
We propose an improved algorithm by identifying and encouraging cooperative behavior in multi-agent environments. First, we analyze the shortcomings of existing algorithms in addressing multi-agent reinforcement learning problems. Then, based on the existing algorithm MADDPG, we introduce a new parameter to increase the reward that an agent can obtain when cooperative behavior among agents is identified. Finally, we compare our improved algorithm with MADDPG in environments from PettingZoo. The results show that the new algorithm helps agents achieve both higher team rewards and individual rewards.
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