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"content": "<p>Edward Tomanek-Volynets' <a href=\"https://mastodon.social/tags/PhD\" class=\"mention hashtag\" rel=\"tag\">#<span>PhD</span></a> <a href=\"https://mastodon.social/tags/research\" class=\"mention hashtag\" rel=\"tag\">#<span>research</span></a> has just been published:</p><p>"The pointer network for reward maximisation in multi-target<br />space mission sequence selection"</p><p>This work uses machine learning to optimise sequences of targets to visit in a multi-target <a href=\"https://mastodon.social/tags/space\" class=\"mention hashtag\" rel=\"tag\">#<span>space</span></a> mission, for example <a href=\"https://mastodon.social/tags/asteroid\" class=\"mention hashtag\" rel=\"tag\">#<span>asteroid</span></a> rendezvous, <a href=\"https://mastodon.social/tags/spacedebris\" class=\"mention hashtag\" rel=\"tag\">#<span>spacedebris</span></a> removal, or <a href=\"https://mastodon.social/tags/satellite\" class=\"mention hashtag\" rel=\"tag\">#<span>satellite</span></a> servicing.</p><p><a href=\"https://mastodon.social/tags/free\" class=\"mention hashtag\" rel=\"tag\">#<span>free</span></a> <a href=\"https://mastodon.social/tags/OpenAccess\" class=\"mention hashtag\" rel=\"tag\">#<span>OpenAccess</span></a>:<br /><a href=\"https://doi.org/10.1016/j.asr.2025.04.045\" target=\"_blank\" rel=\"nofollow noopener\" translate=\"no\"><span class=\"invisible\">https://</span><span class=\"ellipsis\">doi.org/10.1016/j.asr.2025.04.</span><span class=\"invisible\">045</span></a></p><p>Congrats Edward! 🎓</p><p><a href=\"https://mastodon.social/tags/space\" class=\"mention hashtag\" rel=\"tag\">#<span>space</span></a> <a href=\"https://mastodon.social/tags/ML\" class=\"mention hashtag\" rel=\"tag\">#<span>ML</span></a> <a href=\"https://mastodon.social/tags/AI\" class=\"mention hashtag\" rel=\"tag\">#<span>AI</span></a> <a href=\"https://mastodon.social/tags/engineering\" class=\"mention hashtag\" rel=\"tag\">#<span>engineering</span></a> <a href=\"https://mastodon.social/tags/academia\" class=\"mention hashtag\" rel=\"tag\">#<span>academia</span></a> <a href=\"https://mastodon.social/tags/PhD\" class=\"mention hashtag\" rel=\"tag\">#<span>PhD</span></a></p>",
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"name": "Multi-target space mission scenarios such as asteroid rendezvous, debris removal or satellite servicing, require targeting several orbits\nin a single mission, often to be selected among a large set, and therefore choosing optimal sequences of these orbits to be visited. This\npaper demonstrates a reinforcement-learning-based framework for selecting the sequence of targets to be visited in large-scale multi\u0002target mission optimisation problems. The sequence selection is a NP-hard combinatorial optimisation problem. The proposed method\nbuilds upon a neural network architecture for combinatorial optimisation originally developed for Euclidean problems, to produce esti\u0002mates of the optimal sequence of targets in very short amounts of time. The neural network is trained using a policy-gradient\nreinforcement-learning approach. Once training is complete, the network can be evaluated in two ways: one of these (greedy decoding)\nproduces solutions on average 15% less optimal than Ant Colony Optimisation (ACO); the other (stochastic search) is on average 5%\nless optimal than ACO, using an iterative process that is slower than greedy decoding but still orders of magnitude faster than ACO. The\nquality of the network’s solutions is shown both averaged over large amounts of problems, and demonstrated more closely on a few\nspecific instances.",
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