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Discrete Control in Real-World Driving Environments using Deep Reinforcement Learning
Training self-driving cars is often challenging since they require a vast amount of labeled data in multiple real-world contexts, which is computationally and memory intensive.
Analysis of the Self Projected Matching Pursuit Algorithm
The convergence and numerical analysis of a low memory implementation of the Orthogonal Matching Pursuit greedy strategy, which is termed Self Projected Matching Pursuit, is presented.
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