Reinforcement Learning in the Minecraft Gaming Environment 1. Introduction Researchers are keen to solve the challenge of a robot successfully interacting with an external environment. In this regard, the progress made in reinforcement learning (RL), such as Atari 2600 from Google DeepMind, Alpha Go winning the current world champion in the board game Go, and OpenAI winning a 5v5 match against the top players in the world in Dota 2, RL has become a powerful tool to achieve superhuman results in games. RL agents appear to be able to master any game, but what about a game such as Minecraft. The long-term objective of this research is to use RL to teach an agent to survive a day-night cycle in the Minecraft gaming environment. To achieve this, the research tests a new method, referred to as dojo learning based on curriculum learning, against current methods to progress one step closer to the mentioned objective. Although Minecraft is used as a testing platform, the method investigated in this thesis could be generalised and adapted to work in any appropriate gaming environment.
