Modelling the behaviours of other agents is essential for understanding how
agents interact and making effective decisions. Existing methods for agent
modelling commonly assume knowledge of the local observations and chosen
actions of the modelled agents during execution. To eliminate this assumption,
we extract representations from the local information of the controlled agent
using encoder-decoder architectures. Using the observations and actions of the
modelled agents during training, our models learn to extract representations
about the modelled agents conditioned only on the local observations of the
controlled agent. The representations are used to augment the controlled
agent's decision policy which is trained via deep reinforcement learning; thus,
during execution, the policy does not require access to other agents'
information. We provide a comprehensive evaluation and ablations studies in
cooperative, competitive and mixed multi-agent environments, showing that our
method achieves higher returns than baseline methods which do not use the
learned representations.

为了消除现有的管理机构建模中对受控代理的局部信息和选择动作的假设，该研究使用编码器 - 解码器结构从受控代理的局部信息中提取表示，并在训练期间使用管理代理的观察和动作来学习提取关于管理代理的表示，从而增强了管理代理的决策策略。提供了对合作、竞争和混合多代理环境的全面评估和消融研究，并表明我们的方法比不使用学习表示的基线方法产生更高的回报。