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A priori Estimates for Deep Residual Network in Continuous-time Reinforcement Learning
However, existing performance analyses ignores the unique characteristics of continuous-time control problems, is unable to directly estimate the generalization error of the Bellman optimal loss and require a boundedness assumption.
An Experimental Comparison Between Temporal Difference and Residual Gradient with Neural Network Approximation
In this work, we perform extensive experiments to show that TD outperforms RG, that is, when the training leads to a small Bellman residual error, the solution found by TD has a better policy and is more robust against the perturbation of neural network parameters.
FoldingZero: Protein Folding from Scratch in Hydrophobic-Polar Model
In this paper, we propose a novel protein folding framework FoldingZero, self-folding a de novo protein 2D HP structure from scratch based on deep reinforcement learning.
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