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Evaluation of Safety Constraints in Autonomous Navigation with Deep Reinforcement Learning
While reinforcement learning algorithms have had great success in the field of autonomous navigation, they cannot be straightforwardly applied to the real autonomous systems without considering the safety constraints.
Augmenting GRIPS with Heuristic Sampling for Planning Feasible Trajectories of a Car-Like Robot
The results of the experimental evaluation provide a clear evidence that the success rate of the suggested algorithm is up to 40% higher compared to the original GRIPS and hits the bar of 90%, while its runtime is lower.
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