研究人员开发了一个新的框架，用于通过机会约束强化学习进行鲁棒轨迹优化。该方法通过首先离线计算名义轨迹，然后使用强化学习通过自适应控制律对其进行优化来处理初始条件和过程噪声中的不确定性。该方法已在地球-火星转移和火箭着陆等复杂问题上进行了测试，证明了其在不同场景下保持概率可行性和竞争性燃油效率的能力，而无需进行结构重新设计。 AI
影响 该框架可以提高自主系统在复杂、不确定环境中的可靠性和效率。
排序理由 这是一篇详细介绍轨迹优化新算法框架的研究论文。
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This paper presents a distribution-agnostic robust trajectory-optimization framework based on chance-constrained reinforcement learning. The uncertainty is represented here through initial conditions and process noise, with the only requirement being that it can be sampled. A det…
This paper presents a distribution-agnostic robust trajectory-optimization framework based on chance-constrained reinforcement learning. The uncertainty is represented here through initial conditions and process noise, with the only requirement being that it can be sampled. A det…