论文标题
对玩游戏的深入学徒学习
Deep Apprenticeship Learning for Playing Games
论文作者
论文摘要
在过去的十年中，深度学习在机器学习任务中取得了巨大的成功，其中输入数据以不同级别的抽象表示。在最新的使用深神经网络的强化学习研究的驱动下，我们探索了基于专家行为设计学习模型的可行性，这些学习模型是针对奖励功能不可用的复杂，多维任务的。我们根据先前对强化学习中监督学习技术的研究提出了一种新的学徒学习方法。我们的方法应用于Atari游戏的视频帧，以教导人造代理玩这些游戏。即使报告的结果与增强学习的最新结果不可媲美，但我们证明，这种方法有可能在将来取得强大的性能，并且值得进一步研究。
In the last decade, deep learning has achieved great success in machine learning tasks where the input data is represented with different levels of abstractions. Driven by the recent research in reinforcement learning using deep neural networks, we explore the feasibility of designing a learning model based on expert behaviour for complex, multidimensional tasks where reward function is not available. We propose a novel method for apprenticeship learning based on the previous research on supervised learning techniques in reinforcement learning. Our method is applied to video frames from Atari games in order to teach an artificial agent to play those games. Even though the reported results are not comparable with the state-of-the-art results in reinforcement learning, we demonstrate that such an approach has the potential to achieve strong performance in the future and is worthwhile for further research.
