Dynamic Choice of State Abstraction in Q-Learning
Frontiers in Artificial Intelligence and Applications, pp. 46-54, 2016.
Q-learning associates states and actions of a Markov Decision Process to expected future reward through online learning. In practice, however, when the state space is large and experience is still limited, the algorithm will not find a match between current state and experience unless some details describing states are ignored. On the oth...More
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