CraftAssist supplies a Minecraft bot, dialogue interface, and data-recording platform intended to support research on agents that execute tasks specified through conversation.
Deep Reinforcement Learning with Model Learning and Monte Carlo Tree Search in Minecraft
2 Pith papers cite this work. Polarity classification is still indexing.
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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.
fields
cs.AI 2years
2019 2verdicts
UNVERDICTED 2representative citing papers
A rationale is presented for developing an assistant in Minecraft to advance natural language understanding and dialogue learning.
citing papers explorer
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CraftAssist: A Framework for Dialogue-enabled Interactive Agents
CraftAssist supplies a Minecraft bot, dialogue interface, and data-recording platform intended to support research on agents that execute tasks specified through conversation.
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Why Build an Assistant in Minecraft?
A rationale is presented for developing an assistant in Minecraft to advance natural language understanding and dialogue learning.