Adding a k-winners-take-all sparsity mechanism to the hidden layer of a TD-learning network improves performance on three classic control tasks compared to standard backpropagation and linear networks.
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Learning sparse representations in reinforcement learning
Adding a k-winners-take-all sparsity mechanism to the hidden layer of a TD-learning network improves performance on three classic control tasks compared to standard backpropagation and linear networks.