Reinforcement Learning Study Group Report – February 2021
Data Study Group team · Zenodo (CERN European Organization for Nuclear Research) · 2021
Data Study Groups are week-long events at The Alan Turing Institute bringing together some of the country’s top talent from data science, artificial intelligence, and wider fields, to analyse real-world data science challenges. Multi-agent reinforcement learning algorithm performance in unfamiliar domains This document reports on an The Alan Turing Institute Data Study Group (DSG) investigating a Reinforcement Learning (RL) challenge posed by Defence Science and Technology Laboratory (Dstl). Dstl is “the science inside UK defence and security.” As such, they provide evidence required by Defence to make effective decisions. For example, a planning exercise seeks to optimise the use of available resources to achieve a desired effect. Simulations and games can be helpful tools in answering the required questions. 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.