ACRL Chapter Developments

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16-899C ACRL Tetris Reinforcement Learner

Our approach to this problem was to use reinforcement learning with a function approximator to approximate the state value function [RSS98]. In our case, a +1 reward was given for every completed line, so that the value function would encode the long-term number of lines that is going to be completed by the algorithm. In order to achieve this, we extract features from the game state, and use gr...

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ژورنال

عنوان ژورنال: College & Research Libraries News

سال: 1974

ISSN: 2150-6698,0099-0086

DOI: 10.5860/crln.35.10.252