Incremental Truncated LSTD

26 Nov 2015 Clement Gehring Yangchen Pan Martha White

Balancing between computational efficiency and sample efficiency is an important goal in reinforcement learning. Temporal difference (TD) learning algorithms stochastically update the value function, with a linear time complexity in the number of features, whereas least-squares temporal difference (LSTD) algorithms are sample efficient but can be quadratic in the number of features... (read more)

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