We consider the problem of multiple agents sensing and acting in environments with the goal of maximising their shared utility. In these environments, agents must learn communication protocols in order to share information that is needed to solve the tasks. By embracing deep neural networks, we are able to demonstrate end-to-end learning of protocols in complex environments inspired by communication riddles and multi-agent computer vision problems with partial observability. We propose two approaches for learning in these domains: Reinforced Inter-Agent Learning (RIAL) and Differentiable Inter-Agent Learning (DIAL). The former uses deep Q-learning, while the latter exploits the fact that, during learning, agents can propagate error derivatives through (noisy) communication channels. Hence, this approach uses centralised learning but decentralised execution. Our experiments introduce new environments for studying the learning of communication protocols and present a set of engineering innovations that are essential for success in these domains.

本研究基于深度神经网络，提出两种学习策略（RIAL和DIAL），探究在多智能体感知与互动的环境下，通过学习通信协议最大化共享效益的问题。研究表明通过这种中心化学习、分散式执行的方法能够在通信难题和多智能体计算机视觉问题领域中实现端到端的协议学习。

深度多智能体强化学习中的通信学习