TEQL uses a low-rank tensor representation of the Q-function plus error-uncertainty guided exploration to achieve better sample efficiency than matrix low-rank or deep RL baselines on classic control tasks under matched parameter budgets.
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Tensor-Efficient High-Dimensional Q-learning
TEQL uses a low-rank tensor representation of the Q-function plus error-uncertainty guided exploration to achieve better sample efficiency than matrix low-rank or deep RL baselines on classic control tasks under matched parameter budgets.