2Beijing University of Posts and Telecommunications
3The Hong Kong University of Science and Technology
4Microsoft ARD Incubation Team
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.
The overall structure of EncGAT-PreLAC.
From left to right, it uses EncGAT for interaction representation and feeds the representations to the actor
and critic headers. The overall model is first pre-trained with the local loss 𝐿 and then finetuned
with a global loss 𝐿 loc.
The EncGAT model is designed to efficiently learn informative presentations
from the complex interactive graph.
The calculation procedure of the encoder is
based on self-attention, which is similar to the transformer block.
The aggregation among the same type of neighbors is then conducted
through the decoder attention.
In decoder attention, another scaled dot product attention is
applied, which uses the feature vector as the query, and
the encoded neighbors’ features matrix as the keys and values.
The network structure featured three characteristics based on
the problem context of ECR(Empty Containers Repositioning). Firstly, it used the temporal attention to capture
features of the sequential input.
Secondly, we concatenate the edge features with neighbors before feeding it to EncGAT.
Thirdly, both the port and the vessel features are fed into the action head.
Finally, we stack two encoder-decoder attentions in the EncGAT
and residual connections are used to prevent over-smoothing. The
actor and critic headers consist of two fully-connected layers also
with residual connections. We share the headers of actor and local
critic between agents, which makes the overall framework inductive.
More details of our model structure can be found in the MARO Platform.
Full paper and oral presentation accepted by AAMAS-2021
We compare our framework with existing baseline methods with the total fulfillment ratio,
that is the ratio of fulfilled demands to all demands, as the evaluation metric.
We also conduct the ablation studies to answer:
Does the encoder-decoder attention in EncGAT help heterogeneous information
aggregation and intricate interaction understanding?
Does PreLAC improve learning performance of cooperative policy?
Visualization of vertex embeddings in global-critic
(a) and our method (b). The color identifies different ports
and each point represents a feature vector in a batch.
Order proportion distribution between two ports
in the original topology (a) and the new topology (b). Each
column represents the proportions of orders from one port
to orders. Note that two merged ports are the 9th and 10th