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On December 16, 2024 at 11:09:30 PM UTC, admin:
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Changed value of field
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in A Deep Reinforcement Learning Architecture for Multi-stage Optimal Control -
Changed value of field
doi_date_published
to2024-12-16
in A Deep Reinforcement Learning Architecture for Multi-stage Optimal Control -
Added resource Original Metadata to A Deep Reinforcement Learning Architecture for Multi-stage Optimal Control
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3 | "author": "Yuguang Yang", | 3 | "author": "Yuguang Yang", | ||
4 | "author_email": "", | 4 | "author_email": "", | ||
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13 | "extra_authors": [ | 13 | "extra_authors": [ | ||
14 | { | 14 | { | ||
15 | "extra_author": "Johns Hopkins University", | 15 | "extra_author": "Johns Hopkins University", | ||
16 | "orcid": "" | 16 | "orcid": "" | ||
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18 | ], | 18 | ], | ||
19 | "groups": [ | 19 | "groups": [ | ||
20 | { | 20 | { | ||
21 | "description": "", | 21 | "description": "", | ||
22 | "display_name": "Deep Reinforcement Learning", | 22 | "display_name": "Deep Reinforcement Learning", | ||
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33 | "name": "multi-stage-optimal-control", | 33 | "name": "multi-stage-optimal-control", | ||
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44 | "name": | 44 | "name": | ||
45 | -reinforcement-learning-architecture-for-multi-stage-optimal-control", | 45 | -reinforcement-learning-architecture-for-multi-stage-optimal-control", | ||
46 | "notes": "Deep reinforcement learning for high dimensional, | 46 | "notes": "Deep reinforcement learning for high dimensional, | ||
47 | hierarchical control tasks usually requires the use of complex neural | 47 | hierarchical control tasks usually requires the use of complex neural | ||
48 | networks as functional approximators, which can lead to | 48 | networks as functional approximators, which can lead to | ||
49 | inef\ufb01ciency, instability and even divergence in the training | 49 | inef\ufb01ciency, instability and even divergence in the training | ||
50 | process. Here, we introduce stacked deep Q learning (SDQL), a | 50 | process. Here, we introduce stacked deep Q learning (SDQL), a | ||
51 | \ufb02exible modularized deep reinforcement learning architecture, | 51 | \ufb02exible modularized deep reinforcement learning architecture, | ||
52 | that can enable \ufb01nding of optimal control policy of control tasks | 52 | that can enable \ufb01nding of optimal control policy of control tasks | ||
53 | consisting of multiple linear stages in a stable and ef\ufb01cient | 53 | consisting of multiple linear stages in a stable and ef\ufb01cient | ||
54 | way.", | 54 | way.", | ||
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99 | "title": "A Deep Reinforcement Learning Architecture for Multi-stage | 140 | "title": "A Deep Reinforcement Learning Architecture for Multi-stage | ||
100 | Optimal Control", | 141 | Optimal Control", | ||
101 | "type": "dataset", | 142 | "type": "dataset", | ||
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103 | } | 144 | } |