Bootcamp Method for Training General Purpose AI Agents

General purpose agents have long been an ultimate goal of AI research. One promising approach to this goal is to first train an agent to use a variety of skills, called a skillnet agent, and then allow the agent to learn how to choose the appropriate skill instead of having to choose the appropriate low-level action. We propose a method for training skillnet agents called Bootcamp that helps agents efficiently learn basic skills in an environment. We found that Bootcamp agents outperform skillnet agents trained randomly on various tasks defined in the ViZDoom simulated environment. We also found that skillnet agents outperform more conventional reinforcement-based learning approaches such as DQNs in ViZDoom.