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摘要
En 中文
Q(lambda)-learning uses TD(lambda)-methods to accelerate Q-learning. The update complexity of previous online Q(lambda) implementations based on lookup tables is bounded by the size of the state/action space. Our faster algorithm's update complexity is bounded by the number of actions. The method is based on the observation that Q-value updates may be postponed until they are needed.
Keyword:
reinforcement learning
Q-learning
TD(lambda)
online Q(lambda)
lazy learning
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