McKenzie, M.; Loxley, P.; Billingsley, W.; and Wong, S. 2017. Competitive reinforcement learning in atari games. In Australasian Joint Conference on Artificial Intelligence, 14-26. Springer.Mark McKenzie, Peter Loxley, William Billingsley, and Sebastien Wong. Competitive reinforce- ment learning...
Playing Atari Games with Deep Reinforcement Learningand Human Checkpoint ReplayIonel-Alexandru Hosu 1 and Traian Rebedea 2Abstract. This paper introduces a novel method for learning howto play the most diff icult Atari 2600 games from the Arcade Learn-ing Environment using deep reinforcement learning...
模型是一个卷积神经网络,利用 Q-learning的一个变种来进行训练,输入是原始像素,输出是预测将来的奖励的 value function。将此方法应用到 Atari 2600 games 上来,进行测试,发现在所有游戏中都比之前的方法有效,甚至在其中3个游戏中超过了一个人类玩家的水平。 Introduction: 从高维感知输入中学习控制agents,像视觉或者sp...
Proposed Deep-Q-Network, apply to Atari games. Challenge while fusing Reinforcement Learning and Deep Learning: Most successful deep learning applications to date have required large amounts of hand-labelled training data. RL algorithms, on the other hand, must be able to learn from a scalar rew...
Deep reinforcement learning has reinvigorated interest in the popular classic Atari games. Atari games were first released in the 1970s and 1980s and provide a challenging domain for reinforcement learning models. These games are popular among researchers due to their simplicity yet complexity. Deep ...
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从标题可以看出,本文的核心卖点在于"limited data",也就是说在传统意义上,强化学习 (Reinforcement Learning) 是需要大量的数据的,换句话说就是 sample complexity 比较大,而且一些现实中的强化学习场景(例如机器人、自动驾驶等等)中,获得并采集数据的成本是很高的。所以如何在数据集有限的情况下设计强化学习算法,使之...
Reinforcement Learning (RL) has achieved significant milestones in the gaming domain, most notablyGoogleDeepMind’s AlphaGo defeating human Go champion Ken Jie. This victory was also made possible through the Atari Learning Environment (ALE): The ALE has been foundational in RL research, facilitating...
. Wefind that it outperforms all previous approaches on six of the games and surpassesa human expert on three of them.1IntroductionLearning to control agents directly from high-dimensional sensory inputs like vision and speech isone of the long-standing challenges of reinforcement learning (RL)....
We present the first deep learning model to successfully learn control policies directly from high-dimensional sensory input using reinforcement learning. The model is a convolutional neural network, trained with a variant of Q-learning, whose input is r