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

Stephan Alaniz

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arXiv · 1803.08456

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

Stephan Alaniz

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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.

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Stephan Alaniz. 2018-03-22. Deep Reinforcement Learning with Model Learning and Monte Carlo Tree Search in Minecraft. https://arxiv.org/abs/1803.08456

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