In this paper, we investigate how to learn to control a group of cooperative
agents with limited sensing capabilities such as robot swarms. The agents have
only very basic sensor capabilities, yet in a group they can accomplish
sophisticated tasks, such as distributed assembly or search and rescue tasks.
Learning a policy for a group of agents is difficult due to distributed partial
observability of the state. Here, we follow a guided approach where a critic
has central access to the global state during learning, which simplifies the
policy evaluation problem from a reinforcement learning point of view. For
example, we can get the positions of all robots of the swarm using a camera
image of a scene. This camera image is only available to the critic and not to
the control policies of the robots. We follow an actor-critic approach, where
the actors base their decisions only on locally sensed information. In
contrast, the critic is learned based on the true global state. Our algorithm
uses deep reinforcement learning to approximate both the Q-function and the
policy. The performance of the algorithm is evaluated on two tasks with simple
simulated 2D agents: 1) finding and maintaining a certain distance to each
others and 2) locating a target.

本文研究了如何使用有限的传感能力控制一组合作智能体，使用了基于演员 - 评论家算法的深度强化学习以近似 Q 值函数和策略评估，评估了在寻找和维持距离和定位目标方面的性能。