Deep Reinforcement Learning with Model Learning and Monte Carlo Tree Search in Minecraft

Deep reinforcement learning has been successfully applied to several visual-input tasks using model-free methods. In this paper, we propose a model-based approach that combines learning a DNN-based transition model with Monte Carlo tree search to solve a block-placing task in Minecraft. Our learned transition model predicts the next frame and the rewards one step ahead given the last four frames of the agent's first-person-view image and the current action. Then a Monte Carlo tree search algorithm uses this model to plan the best sequence of actions for the agent to perform. On the proposed task in Minecraft, our model-based approach reaches the performance comparable to the Deep Q-Network's, but learns faster and, thus, is more training sample efficient. Keywords: Acknowledgements Reinforcement Learning, Model-Based Reinforcement Learning, Deep Learning, Model Learning, Monte Carlo Tree Search I would like to express my sincere gratitude to my supervisor Dr. Stefan Uhlich for his continuous support, patience, and immense knowledge that helped me a lot during this study. My thanks and appreciation also go to my colleague Anna Konobelkina for insightful comments on the paper as well as to Sony Europe Limited for providing the resources for this project.