Learning Optimal Control Policies for Upright Rocket Landings through Deep Reinforcement Learning

While rocket landing problems are typically solved through conventional trajectory optimization techniques combined with heuristic control, recent developments in deep learning suggest that neural networks are able to approximate the Hamilton-Jacobi-Bellman equation and control spacecraft optimally in real-time with spatial generalization.

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