  Multi-agent reinforcement learning (MARL) has been increasingly explored to
learn the cooperative policy towards maximizing a certain global reward. Many
existing studies take advantage of graph neural networks (GNN) in MARL to
propagate critical collaborative information over the interaction graph, built
upon inter-connected agents. Nevertheless, the vanilla GNN approach yields
substantial defects in dealing with complex real-world scenarios since the
generic message passing mechanism is ineffective between heterogeneous vertices
and, moreover, simple message aggregation functions are incapable of accurately
modeling the combinational interactions from multiple neighbors. While adopting
complex GNN models with more informative message passing and aggregation
mechanisms can obviously benefit heterogeneous vertex representations and
cooperative policy learning, it could, on the other hand, increase the training
difficulty of MARL and demand more intense and direct reward signals compared
to the original global reward. To address these challenges, we propose a new
cooperative learning framework with pre-trained heterogeneous observation
representations. Particularly, we employ an encoder-decoder based graph
attention to learn the intricate interactions and heterogeneous representations
that can be more easily leveraged by MARL. Moreover, we design a pre-training
with local actor-critic algorithm to ease the difficulty in cooperative policy
learning. Extensive experiments over real-world scenarios demonstrate that our
new approach can significantly outperform existing MARL baselines as well as
operational research solutions that are widely-used in industry.
