Decentralized Multi-agent Formation Control via Deep Reinforcement Learning

Multi-agent formation control has been a much-researched topic and while several methods from control theory exist, they require astute expertise to tune properly which is highly resource-intensive and often fails to adapt properly to slight changes in the environment. This paper presents an end-to-end decentralized approach towards multi-agent formation control with the information available from onboard sensors by using a Deep Reinforcement learning framework. The proposed method directly utilizes the raw sensor readings to calculate the agent's movement velocity using a Deep Neural Network. The approach utilizes Policy gradient methods to generalize efficiently on various simulation scenarios and is trained over a large number of agents. We validate the performance of the learned policy using numerous simulated scenarios and a comprehensive evaluation. Finally, the performance of the learned policy is demonstrated in new scenarios with non-cooperative agents that were not introduced during the training process.