Independent Multi-agent Reinforcement Learning Using Common Knowledge

Many recent multi-agent reinforcement learning algorithms used centralized training with decentralized execution (CTDE), which results in a training process that relies on global information and suffers from the dimensional explosion. The independent learning (IL) approaches are simple in structure and can be more easily deployed to a wider range of multi-agent scenarios, but they can only solve relatively simple problems due to environment non-stationarity and partially observable. With this motivation, we let IL agents compute common knowledge information and fuse it with observation to explicitly exploit common knowledge. In addition, we chose a suitable network structure according to the characteristics of IL, using convolutional layers and GRU layers. Based on the above two improvements, we implement two IL algorithms. In our experiments, the algorithms we implemented show significant performance improvements compared to original IL algorithms and further approach CTDE while outperforming multi-agent common knowledge reinforcement learning.