Multi-agent reinforcement learning algorithm performance in unfamiliar domains

Lucia Cipolina-Kun

Abstract: Reinforcement learning (RL) has been shown to be able to generate effective (even super-human) agents to play games such as Go and Starcraft. Dstl would like to investigate RL in the context of games that are relevant in the defence sphere, and in particular whether RL can provide solutions which are adaptable to changes in the configuration o f the game. The aim of this DSG is thus to investigate the effectiveness of RL techniques when the rules of the game change between the training phase and the deployment phase.