Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-06T14:44:08.084140Z
Paper Citation Record · LEDGER
Towards Microgrid Resilience Enhancement via Mobile Power Sources and Repair Crews: A Multi-Agent Reinforcement Learning Approach
As of 16 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 0 inbound Pith citation observations for arXiv:2507.18095.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
43 of 43 outbound references displayed
- verified exact0
- verified fuzzy39
- unresolved4
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 564904a9-a669-4278-971d-2180db90adbf · outbound
This paper cites Battling the extreme: A study on the power system resilience,.
Towards Microgrid Resilience Enhancement via Mobile Power Sources and Repair Crews: A Multi-Agent Reinforcement Learning Approach Battling the extreme: A study on the power system resilience,
Reference 1
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T14:44:09.326022Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 070eb8a3-6433-4c02-a03f-54e51b30a59e · outbound
This paper cites Power system resilience enhancement in typhoons using a three-stage day-ahead unit commitment,.
Towards Microgrid Resilience Enhancement via Mobile Power Sources and Repair Crews: A Multi-Agent Reinforcement Learning Approach Power system resilience enhancement in typhoons using a three-stage day-ahead unit commitment,
Reference 2
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T14:44:09.319305Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 7e835aa4-dbf1-4f86-8bed-8780707c5292 · outbound
This paper cites Seismic-resilient electric power distribution systems: Harnessing the mobility of power sources,.
Towards Microgrid Resilience Enhancement via Mobile Power Sources and Repair Crews: A Multi-Agent Reinforcement Learning Approach Seismic-resilient electric power distribution systems: Harnessing the mobility of power sources,
Reference 3
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T14:44:09.312596Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 4b42eae9-4e90-46e5-af24-e27c56a65341 · outbound
This paper cites On microgrids and resilience: A comprehensive review on modeling and operational strategies,.
Towards Microgrid Resilience Enhancement via Mobile Power Sources and Repair Crews: A Multi-Agent Reinforcement Learning Approach On microgrids and resilience: A comprehensive review on modeling and operational strategies,
Reference 4
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T14:44:09.306689Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 41421fd0-b171-4f66-89f8-20fdc9f6513c · outbound
This paper cites Optimizing service restoration in distribution systems with uncertain repair time and demand,.
Towards Microgrid Resilience Enhancement via Mobile Power Sources and Repair Crews: A Multi-Agent Reinforcement Learning Approach Optimizing service restoration in distribution systems with uncertain repair time and demand,
Reference 5
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T14:44:09.300471Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation de4e7c58-3f04-4881-9bdf-e1b10d461027 · outbound
This paper cites Hybrid modeling based co-optimization of crew dispatch and distribution system restoration considering multiple uncertainties,.
Towards Microgrid Resilience Enhancement via Mobile Power Sources and Repair Crews: A Multi-Agent Reinforcement Learning Approach Hybrid modeling based co-optimization of crew dispatch and distribution system restoration considering multiple uncertainties,
Reference 6
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T14:44:09.293990Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation b2b2097e-cee6-4353-994d-d6d66069fd12 · outbound
This paper cites Mobile emergency generator pre-positioning and real-time allocation for resilient response to natural disasters,.
Towards Microgrid Resilience Enhancement via Mobile Power Sources and Repair Crews: A Multi-Agent Reinforcement Learning Approach Mobile emergency generator pre-positioning and real-time allocation for resilient response to natural disasters,
Reference 7
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T14:44:09.287891Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 9290afaf-4737-48f0-a81f-7011756f77db · outbound
This paper cites Mobile emergency generator planning in resilient distribution systems: A three-stage stochastic model with nonanticipativity constraints,.
Towards Microgrid Resilience Enhancement via Mobile Power Sources and Repair Crews: A Multi-Agent Reinforcement Learning Approach Mobile emergency generator planning in resilient distribution systems: A three-stage stochastic model with nonanticipativity constraints,
Reference 8
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T14:44:09.281801Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 9c170fe3-f53f-4443-bae8-f4887396338e · outbound
This paper cites Resilience-driven optimal sizing and pre-positioning of mobile energy storage systems in decentralized networked microgrids,.
Towards Microgrid Resilience Enhancement via Mobile Power Sources and Repair Crews: A Multi-Agent Reinforcement Learning Approach Resilience-driven optimal sizing and pre-positioning of mobile energy storage systems in decentralized networked microgrids,
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T14:44:09.274592Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation f5de6a67-bf0a-412c-b7f2-e7c01d5075ca · outbound
This paper cites Routing and scheduling of mobile power sources for distribution system resilience enhancement,.
Towards Microgrid Resilience Enhancement via Mobile Power Sources and Repair Crews: A Multi-Agent Reinforcement Learning Approach Routing and scheduling of mobile power sources for distribution system resilience enhancement,
Reference 10
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T14:44:09.266752Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation d5da2621-a86b-4a93-9e2e-b1c67911ddc5 · outbound
This paper cites Resilient disaster recovery logistics of distribution systems: Co-optimize service restoration with repair crew and mobile power source dispatch,.
Towards Microgrid Resilience Enhancement via Mobile Power Sources and Repair Crews: A Multi-Agent Reinforcement Learning Approach Resilient disaster recovery logistics of distribution systems: Co-optimize service restoration with repair crew and mobile power source dispatch,
Reference 11
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T14:44:09.259667Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 9dfe08e2-9201-4ede-8ad7-95eeb3bd22a9 · outbound
This paper cites Resilient service restoration for unbalanced distribution systems with distributed energy resources by leveraging mobile generators,.
Towards Microgrid Resilience Enhancement via Mobile Power Sources and Repair Crews: A Multi-Agent Reinforcement Learning Approach Resilient service restoration for unbalanced distribution systems with distributed energy resources by leveraging mobile generators,
Reference 12
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T14:44:09.251346Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 863a0f22-a4f6-4a87-8ed1-028434515375 · outbound
This paper cites Multiperiod distribution system restoration with routing repair crews, mobile electric vehicles, and soft-open-point networked microgrids,.
Towards Microgrid Resilience Enhancement via Mobile Power Sources and Repair Crews: A Multi-Agent Reinforcement Learning Approach Multiperiod distribution system restoration with routing repair crews, mobile electric vehicles, and soft-open-point networked microgrids,
Reference 13
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T14:44:09.241978Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation e220f412-fa65-4add-b7b3-f6e11a842c38 · outbound
This paper cites Stochastic pre-event prepa- ration for enhancing resilience of distribution systems,.
Towards Microgrid Resilience Enhancement via Mobile Power Sources and Repair Crews: A Multi-Agent Reinforcement Learning Approach Stochastic pre-event prepa- ration for enhancing resilience of distribution systems,
Reference 14
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T14:44:09.234228Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 7191b6a6-3c00-4d05-9379-4604a9d371a7 · outbound
This paper cites Multi-period restoration model for integrated power-hydrogen systems considering transportation states,.
Towards Microgrid Resilience Enhancement via Mobile Power Sources and Repair Crews: A Multi-Agent Reinforcement Learning Approach Multi-period restoration model for integrated power-hydrogen systems considering transportation states,
Reference 15
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T14:44:09.225996Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation d86c70ff-429a-4369-abf8-6d72f6a54b48 · outbound
This paper cites A sequential black-start restoration model for resilient active distribution networks,.
Towards Microgrid Resilience Enhancement via Mobile Power Sources and Repair Crews: A Multi-Agent Reinforcement Learning Approach A sequential black-start restoration model for resilient active distribution networks,
Reference 16
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T14:44:09.219246Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation ec508d92-d3d4-412a-859a-59fbb07f48bb · outbound
This paper cites A new model for resilient distribution systems by microgrids formation,.
Towards Microgrid Resilience Enhancement via Mobile Power Sources and Repair Crews: A Multi-Agent Reinforcement Learning Approach A new model for resilient distribution systems by microgrids formation,
Reference 17
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T14:44:09.211287Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation e94e7da9-9658-41c8-a980-f3d2f5386b03 · outbound
This paper cites A resilient microgrid formation strategy for load restoration considering master-slave distributed genera- tors and topology reconfiguration,.
Towards Microgrid Resilience Enhancement via Mobile Power Sources and Repair Crews: A Multi-Agent Reinforcement Learning Approach A resilient microgrid formation strategy for load restoration considering master-slave distributed genera- tors and topology reconfiguration,
Reference 18
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T14:44:09.202602Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation ead924e4-967a-4672-92eb-2fe2429df70f · outbound
This paper cites A full decentralized multi-agent service restoration for distribution network with dgs,.
Towards Microgrid Resilience Enhancement via Mobile Power Sources and Repair Crews: A Multi-Agent Reinforcement Learning Approach A full decentralized multi-agent service restoration for distribution network with dgs,
Reference 19
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T14:44:09.194292Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 71ea2c5c-4d56-4b67-ba67-1f59c4e17f7d · outbound
This paper cites A resilience-oriented centralised-to-decentralised framework for networked microgrids man- agement,.
Towards Microgrid Resilience Enhancement via Mobile Power Sources and Repair Crews: A Multi-Agent Reinforcement Learning Approach A resilience-oriented centralised-to-decentralised framework for networked microgrids man- agement,
Reference 20
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T14:44:09.185309Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 5f23e7b1-3638-4a69-bf53-720d163116c8 · outbound
This paper cites an unresolved cited work.
Towards Microgrid Resilience Enhancement via Mobile Power Sources and Repair Crews: A Multi-Agent Reinforcement Learning Approach Unresolved cited work
Reference 21
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T14:44:05.643181Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 4bcefbea-347e-4dee-83a5-7c3abddc73c1 · outbound
This paper cites Distribution system resilience under asynchronous information using deep reinforcement learning,.
Towards Microgrid Resilience Enhancement via Mobile Power Sources and Repair Crews: A Multi-Agent Reinforcement Learning Approach Distribution system resilience under asynchronous information using deep reinforcement learning,
Reference 22
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T14:44:09.173896Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation fda80481-86d4-42c4-ab02-e02f41a6f23d · outbound
This paper cites Deep reinforcement learning based model-free on-line dynamic multi-microgrid formation to enhance resilience,.
Towards Microgrid Resilience Enhancement via Mobile Power Sources and Repair Crews: A Multi-Agent Reinforcement Learning Approach Deep reinforcement learning based model-free on-line dynamic multi-microgrid formation to enhance resilience,
Reference 23
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T14:44:09.167293Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 2c20145f-5c4c-4d4f-8edc-385fec5be849 · outbound
This paper cites A deep reinforce- ment learning-based multi-agent framework to enhance power system resilience using shunt resources,.
Towards Microgrid Resilience Enhancement via Mobile Power Sources and Repair Crews: A Multi-Agent Reinforcement Learning Approach A deep reinforce- ment learning-based multi-agent framework to enhance power system resilience using shunt resources,
Reference 24
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T14:44:09.160685Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation c1fd9916-2e61-4d17-9608-f030ac48f760 · outbound
This paper cites Resilient load restoration in microgrids considering mobile energy storage fleets: A deep reinforcement learning approach,.
Towards Microgrid Resilience Enhancement via Mobile Power Sources and Repair Crews: A Multi-Agent Reinforcement Learning Approach Resilient load restoration in microgrids considering mobile energy storage fleets: A deep reinforcement learning approach,
Reference 25
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T14:44:09.151547Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 21312d87-6ff0-43b2-a7fc-307050df121e · outbound
This paper cites Multi-agent safe policy learning for power management of networked microgrids,.
Towards Microgrid Resilience Enhancement via Mobile Power Sources and Repair Crews: A Multi-Agent Reinforcement Learning Approach Multi-agent safe policy learning for power management of networked microgrids,
Reference 26
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T14:44:09.143588Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation db581155-79e8-4e35-b10a-a41de20ad792 · outbound
This paper cites Multi-agent deep reinforcement learning for resilience-driven routing and scheduling of mobile energy storage systems,.
Towards Microgrid Resilience Enhancement via Mobile Power Sources and Repair Crews: A Multi-Agent Reinforcement Learning Approach Multi-agent deep reinforcement learning for resilience-driven routing and scheduling of mobile energy storage systems,
Reference 27
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T14:44:09.136077Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation ca6dffcd-d95d-46cb-ad50-d872e2c21520 · outbound
This paper cites A three-level planning model for optimal sizing of networked microgrids considering a trade-off between resilience and cost,.
Towards Microgrid Resilience Enhancement via Mobile Power Sources and Repair Crews: A Multi-Agent Reinforcement Learning Approach A three-level planning model for optimal sizing of networked microgrids considering a trade-off between resilience and cost,
Reference 28
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T14:44:09.127796Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 8c6d810c-9473-4286-a09e-8940d51f4119 · outbound
This paper cites Research on resilience of power systems under natural disasters—a review,.
Towards Microgrid Resilience Enhancement via Mobile Power Sources and Repair Crews: A Multi-Agent Reinforcement Learning Approach Research on resilience of power systems under natural disasters—a review,
Reference 29
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T14:44:09.120781Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation fdb57cf6-c437-4e94-b5f9-9e956a39754c · outbound
This paper cites Theory and application study of the road traffic impedance function,.
Towards Microgrid Resilience Enhancement via Mobile Power Sources and Repair Crews: A Multi-Agent Reinforcement Learning Approach Theory and application study of the road traffic impedance function,
Reference 30
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T14:44:09.113888Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 1b760787-dc8b-4b0e-910c-34a1d1738298 · outbound
This paper cites Network reconfiguration in distribution systems for loss reduction and load balancing,.
Towards Microgrid Resilience Enhancement via Mobile Power Sources and Repair Crews: A Multi-Agent Reinforcement Learning Approach Network reconfiguration in distribution systems for loss reduction and load balancing,
Reference 31
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T14:44:09.106529Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 5623dd97-3af2-4036-a94e-8e2ebb11fca0 · outbound
This paper cites A two-level simulation- assisted sequential distribution system restoration model with frequency dynamics constraints,.
Towards Microgrid Resilience Enhancement via Mobile Power Sources and Repair Crews: A Multi-Agent Reinforcement Learning Approach A two-level simulation- assisted sequential distribution system restoration model with frequency dynamics constraints,
Reference 32
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T14:44:09.098850Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 59e9f1c0-d2ae-42b3-9112-1c00abfa3284 · outbound
This paper cites an unresolved cited work.
Towards Microgrid Resilience Enhancement via Mobile Power Sources and Repair Crews: A Multi-Agent Reinforcement Learning Approach Unresolved cited work
Reference 33
- Resolution
- unresolved
- raw_fallback, observed 2026-08-06T14:44:09.091328Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation be706a7e-743d-4d18-9d1b-27ba91178454 · outbound
This paper cites A learning-based power management method for networked microgrids under incomplete information,.
Towards Microgrid Resilience Enhancement via Mobile Power Sources and Repair Crews: A Multi-Agent Reinforcement Learning Approach A learning-based power management method for networked microgrids under incomplete information,
Reference 34
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T14:44:09.082623Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 97e181bd-6554-4a6c-a43e-6c89d08d3b15 · outbound
This paper cites Estimating demand flexibility using siamese lstm neural networks,.
Towards Microgrid Resilience Enhancement via Mobile Power Sources and Repair Crews: A Multi-Agent Reinforcement Learning Approach Estimating demand flexibility using siamese lstm neural networks,
Reference 35
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T14:44:09.075166Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation f25a2a6e-f657-407e-b294-5dc69b2b96cd · outbound
This paper cites A hybrid of deep reinforcement learning and local search for the vehicle routing problems,.
Towards Microgrid Resilience Enhancement via Mobile Power Sources and Repair Crews: A Multi-Agent Reinforcement Learning Approach A hybrid of deep reinforcement learning and local search for the vehicle routing problems,
Reference 36
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T14:44:09.067765Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation dc21cec3-2450-4073-a9c7-e9e056ec7d62 · outbound
This paper cites Real-time operation management for battery swapping-charging system via multi-agent deep reinforcement learning,.
Towards Microgrid Resilience Enhancement via Mobile Power Sources and Repair Crews: A Multi-Agent Reinforcement Learning Approach Real-time operation management for battery swapping-charging system via multi-agent deep reinforcement learning,
Reference 37
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T14:44:09.058332Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 2b3a043b-23d1-4ef1-a5d3-4252712874c2 · outbound
This paper cites Charging cost aware fleet manage- ment for shared on-demand green logistic system,.
Towards Microgrid Resilience Enhancement via Mobile Power Sources and Repair Crews: A Multi-Agent Reinforcement Learning Approach Charging cost aware fleet manage- ment for shared on-demand green logistic system,
Reference 38
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T14:44:09.048822Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 4e397b75-6034-47f9-9bbe-be2aab9fddd4 · outbound
This paper cites an unresolved cited work.
Towards Microgrid Resilience Enhancement via Mobile Power Sources and Repair Crews: A Multi-Agent Reinforcement Learning Approach Unresolved cited work
Reference 39
- Resolution
- unresolved
- raw_fallback, observed 2026-08-06T14:44:08.935050Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 581d3924-92a7-4517-ab6e-27b0fdb53ad8 · outbound
This paper cites Hybrid multi-agent reinforcement learning for electric vehicle resilience control towards a low-carbon transition,.
Towards Microgrid Resilience Enhancement via Mobile Power Sources and Repair Crews: A Multi-Agent Reinforcement Learning Approach Hybrid multi-agent reinforcement learning for electric vehicle resilience control towards a low-carbon transition,
Reference 40
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T14:44:08.723114Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 48cb6cb2-a50a-4238-93e9-d9597dc2f5ee · outbound
This paper cites The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Games.
Towards Microgrid Resilience Enhancement via Mobile Power Sources and Repair Crews: A Multi-Agent Reinforcement Learning Approach The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Games
Reference 41
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T14:44:07.828501Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 116e38d6-2a14-4b46-9d0a-5c29f7ee37c8 · outbound
This paper cites Between mdps and semi-mdps: A framework for temporal abstraction in reinforcement learning,.
Towards Microgrid Resilience Enhancement via Mobile Power Sources and Repair Crews: A Multi-Agent Reinforcement Learning Approach Between mdps and semi-mdps: A framework for temporal abstraction in reinforcement learning,
Reference 42
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T14:44:08.503927Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 47f01509-4b76-443b-b117-da043d5ec7b5 · outbound
This paper cites Residential load and rooftop pv generation: an australian distribution network dataset,.
Towards Microgrid Resilience Enhancement via Mobile Power Sources and Repair Crews: A Multi-Agent Reinforcement Learning Approach Residential load and rooftop pv generation: an australian distribution network dataset,
Reference 43
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T14:44:08.266399Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Pith citing papers
No inbound Pith citation observations are available.