Deep Reinforcement Learning with Model Learning and Monte Carlo Tree Search in Minecraft
Abstract
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.
Cite
@article{arxiv.1803.08456,
title = {Deep Reinforcement Learning with Model Learning and Monte Carlo Tree Search in Minecraft},
author = {Stephan Alaniz},
journal= {arXiv preprint arXiv:1803.08456},
year = {2018}
}
Comments
The 3rd Multidisciplinary Conference on Reinforcement Learning and Decision Making (RLDM) 2017