An Improved Multi-Agent Algorithm for Cooperative and Competitive Environments by Identifying and Encouraging Cooperation among Agents
This work addresses multi-agent coordination in reinforcement learning, but it appears incremental as it builds directly on the existing MADDPG method.
The paper tackled the problem of improving multi-agent reinforcement learning by identifying and encouraging cooperative behavior among agents, resulting in higher team and individual rewards compared to the baseline MADDPG algorithm in PettingZoo environments.
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.