Teacher:In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.
Teacher: Now, understand the problem? Solve this instance: We consider the problem of multiple agents sensing and acting in environments<lb>with the goal of maximising their shared utility. In these environments, agents must<lb>learn communication protocols in order to share information that is needed to solve<lb>the tasks. By embracing deep neural networks, we are able to demonstrate end-<lb>to-end learning of protocols in complex environments inspired by communication<lb>riddles and multi-agent computer vision problems with partial observability. We<lb>propose two approaches for learning in these domains: Reinforced Inter-Agent<lb>Learning (RIAL) and Differentiable Inter-Agent Learning (DIAL). The former uses<lb>deep Q-learning, while the latter exploits the fact that, during learning, agents can<lb>backpropagate error derivatives through (noisy) communication channels. Hence,<lb>this approach uses centralised learning but decentralised execution. Our experi-<lb>ments introduce new environments for studying the learning of communication<lb>protocols and present a set of engineering innovations that are essential for success<lb>in these domains.
Student:
Learning to Communicate with Deep Multi-Agent Reinforcement Learning