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Paper Citation Record · LEDGER

Towards Fault Tolerance in Multi-Agent Reinforcement Learning

As of 15 August 2026, this Paper Citation Record lists 54 of 54 outbound references and 1 inbound Pith citation observation for arXiv:2412.00534.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2412.00534 v1

Coverage vector

measured 54 of 54 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T05:22:09.722683Z

measured 55 of 55 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T19:28:52.274750Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-08-11T19:28:52.638301Z

Reference resolution

54 of 54 outbound references displayed

  • verified exact1
  • verified fuzzy41
  • unresolved12
  • parse uncertain0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 0ef39b6a-8f2d-4f3d-bc95-80bef705377f · outbound

This paper cites Deep reinforcement learning for autonomous driving: A survey,.

Towards Fault Tolerance in Multi-Agent Reinforcement Learning Deep reinforcement learning for autonomous driving: A survey,

Reference 1

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation 02149e4c-a161-4e55-b481-7c18aa4cb1ba · outbound

This paper cites Distributed multi-vehicle task assignment and motion planning in dense environments,.

Towards Fault Tolerance in Multi-Agent Reinforcement Learning Distributed multi-vehicle task assignment and motion planning in dense environments,

Reference 2

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation 8b42275d-fba1-4b60-b9fc-5787d72d0022 · outbound

This paper cites A survey on multi- agent reinforcement learning applications in the internet of vehicles,.

Towards Fault Tolerance in Multi-Agent Reinforcement Learning A survey on multi- agent reinforcement learning applications in the internet of vehicles,

Reference 3

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T05:22:10.514197Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation 8cb1b92e-4780-4df8-b52e-499139071318 · outbound

This paper cites Theory and experiment on formation-containment control of multiple multirotor unmanned aerial vehicle systems,.

Towards Fault Tolerance in Multi-Agent Reinforcement Learning Theory and experiment on formation-containment control of multiple multirotor unmanned aerial vehicle systems,

Reference 4

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T05:22:10.498862Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T05:22:09.482163Z digest=sha256:6f9935df490ae4f67d855be73d3a1b07f49d082b5111c1e668f84e9b30fa1f8c

Observation f0306bee-2680-44c6-b6b5-6a3e1a81ed80 · outbound

This paper cites Cooperative internet of uavs: Distributed trajectory design by multi-agent deep reinforcement learning,.

Towards Fault Tolerance in Multi-Agent Reinforcement Learning Cooperative internet of uavs: Distributed trajectory design by multi-agent deep reinforcement learning,

Reference 5

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T05:22:10.483609Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T05:22:09.487265Z digest=sha256:b547c7fa5219b9a990d2917fb43f3b653d81b31af5cbee873d1bfa3a2489fc3f

Observation 5604ac6e-8397-4079-b61e-59edd446b198 · outbound

This paper cites Heterogeneous multi-robot reinforcement learning,.

Towards Fault Tolerance in Multi-Agent Reinforcement Learning Heterogeneous multi-robot reinforcement learning,

Reference 6

Resolution
unresolved
no resolver link, observed 2026-08-12T05:22:09.492232Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T05:22:09.492232Z digest=sha256:7f8e2030f094cef1b2036b8228765a48f22df48054b8d87a50877c7aa2d5dca7

Observation fd591eb3-54e0-4f71-b9f6-45b77ed9788b · outbound

This paper cites On the effects of communication failures in a multi-agent consensus network,.

Towards Fault Tolerance in Multi-Agent Reinforcement Learning On the effects of communication failures in a multi-agent consensus network,

Reference 7

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T05:22:09.497616Z digest=sha256:88ac92eb509bfdd6999c50e04c95941b5fc9f96666982791b1b4fe42dd2860a2

Observation a10027c5-14bf-48ec-bc2d-5f621d29fc50 · outbound

This paper cites The impact of agent definitions and interactions on multiagent learning for coordination in traffic management domains,.

Towards Fault Tolerance in Multi-Agent Reinforcement Learning The impact of agent definitions and interactions on multiagent learning for coordination in traffic management domains,

Reference 8

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T05:22:10.444134Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation 42d4a4d0-ae76-404d-babf-db761ca0b3e6 · outbound

This paper cites Fault-tolerant cooperative control of multiagent systems: A survey of trends and methodologies,.

Towards Fault Tolerance in Multi-Agent Reinforcement Learning Fault-tolerant cooperative control of multiagent systems: A survey of trends and methodologies,

Reference 9

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T05:22:10.428546Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation aaaee778-8470-425c-9848-1389f31a27fa · outbound

This paper cites Fault-tolerant cooperative control of multiagent systems: A survey of trends and methodologies,.

Towards Fault Tolerance in Multi-Agent Reinforcement Learning Fault-tolerant cooperative control of multiagent systems: A survey of trends and methodologies,

Reference 10

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T05:22:10.413639Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T05:22:09.512052Z digest=sha256:07d6f923c4dd2eb9d588b2b36eadd3cded2167a477ce2826b5990e86452631e3

Observation d88154f3-590d-4c30-9103-2a4680edbef8 · outbound

This paper cites Fault-tolerant consensus of leader–following multi-agent systems with jointly connected topologies,.

Towards Fault Tolerance in Multi-Agent Reinforcement Learning Fault-tolerant consensus of leader–following multi-agent systems with jointly connected topologies,

Reference 11

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T05:22:09.516793Z digest=sha256:eb18261b989988fb662713fad5d6b139cc018aa35f3335db12e8f4c2afa54474

Observation f8c816c0-6ecb-4262-8606-061d5dc73661 · outbound

This paper cites Fault-tolerant formation for multi-uav via improved artificial potential field method,.

Towards Fault Tolerance in Multi-Agent Reinforcement Learning Fault-tolerant formation for multi-uav via improved artificial potential field method,

Reference 12

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T05:22:09.521317Z digest=sha256:de328e44cc82206aa6392a3755a9f50a08da391fd519554a36275c4fa46dff59

Observation 258fada4-c201-43a9-8e77-01c1bad04081 · outbound

This paper cites Policy gradient methods for reinforcement learning with function approximation,.

Towards Fault Tolerance in Multi-Agent Reinforcement Learning Policy gradient methods for reinforcement learning with function approximation,

Reference 13

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T05:22:09.526089Z digest=sha256:7f7a73f59735b4716157b27688f78143936c386021bc09e59d8f2070824f0a5a

Observation 1334f93f-0e8b-4e12-9a4b-1aa40f17bb29 · outbound

This paper cites Multi-agent reinforcement learning with decentral- ized distribution correction,.

Towards Fault Tolerance in Multi-Agent Reinforcement Learning Multi-agent reinforcement learning with decentral- ized distribution correction,

Reference 14

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T05:22:09.530793Z digest=sha256:24e370faddfd45227aeb79cc242de190a6af8028a9d03a2b77704d5a5e86f5f0

Observation 6171fcb6-9ed1-494d-9225-7a9c5e2d6209 · outbound

This paper cites Multi-agent actor-critic for mixed cooperative-competitive environments,.

Towards Fault Tolerance in Multi-Agent Reinforcement Learning Multi-agent actor-critic for mixed cooperative-competitive environments,

Reference 15

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T05:22:09.535322Z digest=sha256:6e00904ef846a55f7af4b168930f90b87b9acaf651a7dee7d9578a70b8c3feed

Observation 9d2b276e-728d-488a-8681-3dc6d323c42e · outbound

This paper cites The surprising effectiveness of ppo in cooperative multi-agent games,.

Towards Fault Tolerance in Multi-Agent Reinforcement Learning The surprising effectiveness of ppo in cooperative multi-agent games,

Reference 16

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T05:22:09.539877Z digest=sha256:1755ef5c8548db89c1b31f4d1f6e28b8c108f24167dea2a0f4c1c95774ec099d

Observation 14218bdb-2ea6-48bb-9e11-be1778733a3c · outbound

This paper cites Prioritized experience replay,.

Towards Fault Tolerance in Multi-Agent Reinforcement Learning Prioritized experience replay,

Reference 17

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation 9ad8e1a3-40cf-42ff-b466-9bee99a61da5 · outbound

This paper cites Towards a fault-tolerant multi-agent system architecture,.

Towards Fault Tolerance in Multi-Agent Reinforcement Learning Towards a fault-tolerant multi-agent system architecture,

Reference 18

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation 4e52e42a-8e2f-4624-9559-b0d513c7ee8a · outbound

This paper cites A survey on fault tolerant multi agent system,.

Towards Fault Tolerance in Multi-Agent Reinforcement Learning A survey on fault tolerant multi agent system,

Reference 19

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T05:22:09.553810Z digest=sha256:2ad301b06a119f542ca9dab385438e3987fe3d43747a9ac1f1ec88229f367fae

Observation c2fb3118-444a-4c1d-87aa-8a0c187705c7 · outbound

This paper cites Adaptive fault-tolerant boundary control of an autonomous aerial refueling hose system with prescribed constraints,.

Towards Fault Tolerance in Multi-Agent Reinforcement Learning Adaptive fault-tolerant boundary control of an autonomous aerial refueling hose system with prescribed constraints,

Reference 20

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T05:22:09.558575Z digest=sha256:2a39601b5f94dd94b7be5b5aec5e970d058a78910303021893c08098e5fd4655

Observation 3f25025c-149c-4ea3-b7ed-07163fbf7e69 · outbound

This paper cites A goa-based fault-tolerant trajectory tracking control for an underwater vehicle of multi-thruster system without actuator saturation,.

Towards Fault Tolerance in Multi-Agent Reinforcement Learning A goa-based fault-tolerant trajectory tracking control for an underwater vehicle of multi-thruster system without actuator saturation,

Reference 21

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation 3bb7a928-8527-43a1-b731-49130c1e179a · outbound

This paper cites Fault-tolerant cooperative driving at signal-free intersections,.

Towards Fault Tolerance in Multi-Agent Reinforcement Learning Fault-tolerant cooperative driving at signal-free intersections,

Reference 22

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation 3abc5b53-d14b-4aac-a2ab-d4ff4f9f6519 · outbound

This paper cites Fault-tolerant cooperative control design of multiple wheeled mobile robots,.

Towards Fault Tolerance in Multi-Agent Reinforcement Learning Fault-tolerant cooperative control design of multiple wheeled mobile robots,

Reference 23

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation afa9543b-2763-4f44-9669-63ff2735f6ce · outbound

This paper cites Robust multi- agent reinforcement learning via minimax deep deterministic policy gradient,.

Towards Fault Tolerance in Multi-Agent Reinforcement Learning Robust multi- agent reinforcement learning via minimax deep deterministic policy gradient,

Reference 24

Resolution
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raw_fallback, observed 2026-08-12T05:22:10.199425Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T05:22:09.576878Z digest=sha256:cb8ca36021c653782f181fc0cb81849862a81186665ec5ba1f0e10877fae8d3a

Observation bd5bb53a-38a5-4ccd-a9eb-65007ce2c0ec · outbound

This paper cites Byzantine Robust Cooperative Multi-Agent Reinforcement Learning as a Bayesian Game.

Towards Fault Tolerance in Multi-Agent Reinforcement Learning Byzantine Robust Cooperative Multi-Agent Reinforcement Learning as a Bayesian Game

Reference 25

Resolution
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no resolver link, observed 2026-08-12T05:22:09.581417Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T05:22:09.581417Z digest=sha256:653bef88b7fc433f3f1a92471d719b25412bf81d2ac8aef4e84d34c13c0dacf3

Observation 0ef13575-f180-404c-af0e-ae2b6765cf49 · outbound

This paper cites QMIX: Monotonic value function factorisation for deep multi-agent reinforcement learning,.

Towards Fault Tolerance in Multi-Agent Reinforcement Learning QMIX: Monotonic value function factorisation for deep multi-agent reinforcement learning,

Reference 26

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T05:22:10.184154Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T05:22:09.586181Z digest=sha256:585692abf60be56eec248d64989dde20ca22a88465a473cccd8533a37390e8e1

Observation 2d9f5118-7c3c-44df-8d55-2387f9483969 · outbound

This paper cites Counterfactual multi-agent policy gradients,.

Towards Fault Tolerance in Multi-Agent Reinforcement Learning Counterfactual multi-agent policy gradients,

Reference 27

Resolution
unresolved
no resolver link, observed 2026-08-12T05:22:09.590610Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T05:22:09.590610Z digest=sha256:687199f3de9ff099f39b2e31628292b2b6624b764de7c1695528209fc8aaa645

Observation 4b937b0c-95be-456f-a81c-23adbaeac636 · outbound

This paper cites Evolutionary population curriculum for scaling multi-agent reinforcement learning,.

Towards Fault Tolerance in Multi-Agent Reinforcement Learning Evolutionary population curriculum for scaling multi-agent reinforcement learning,

Reference 28

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T05:22:10.159221Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T05:22:09.595066Z digest=sha256:dbdcd79c2d17219454b3ec62335ac2e259b5a21832a66d9c6a28b4d781c6ffcd

Observation 8c3d8768-4139-49f6-81e1-8c999dccd855 · outbound

This paper cites Scalable autonomous separation assurance with heterogeneous multi-agent reinforcement learning,.

Towards Fault Tolerance in Multi-Agent Reinforcement Learning Scalable autonomous separation assurance with heterogeneous multi-agent reinforcement learning,

Reference 29

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T05:22:10.144161Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T05:22:09.599722Z digest=sha256:8b115025034ad0f9a3dd2cc14544bcbf91a7539c8e34a502262d46310aad933d

Observation 6968ca23-d612-4128-9164-b8c59ebb838c · outbound

This paper cites Asynchronous multi-agent reinforcement learning for efficient real-time multi-robot cooperative exploration,.

Towards Fault Tolerance in Multi-Agent Reinforcement Learning Asynchronous multi-agent reinforcement learning for efficient real-time multi-robot cooperative exploration,

Reference 30

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T05:22:10.127665Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T05:22:09.604174Z digest=sha256:1af68f2ac8d0c476abf398802066714121f42c0dfab950327a979591d733eab5

Observation 70b76b4f-0de7-49e7-b5b3-02d52f148a6d · outbound

This paper cites Race: improve multi-agent reinforcement learning with representation asymmetry and collaborative evolution,.

Towards Fault Tolerance in Multi-Agent Reinforcement Learning Race: improve multi-agent reinforcement learning with representation asymmetry and collaborative evolution,

Reference 31

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T05:22:10.110615Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T05:22:09.608839Z digest=sha256:3e373bc7e55866a7b8037e05f993e572718f7a80ecdc183d4cfe743603cd1568

Observation 495f4bb4-5859-4281-9b9d-fafc1d7c8091 · outbound

This paper cites Effective multi-agent deep reinforcement learning control with relative entropy regularization,.

Towards Fault Tolerance in Multi-Agent Reinforcement Learning Effective multi-agent deep reinforcement learning control with relative entropy regularization,

Reference 32

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T05:22:10.095039Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T05:22:09.613216Z digest=sha256:52b61b1cbc0e112f01d75ed0220a6e760d469b8a68d026bbf5951cb341416555

Observation c1eaa6fb-d84c-45e4-8f53-fcc45f27b21d · outbound

This paper cites Multi-task multi- agent reinforcement learning with task-entity transformers and value decomposition training,.

Towards Fault Tolerance in Multi-Agent Reinforcement Learning Multi-task multi- agent reinforcement learning with task-entity transformers and value decomposition training,

Reference 33

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T05:22:10.079621Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T05:22:09.618034Z digest=sha256:4309c1f4584d240419460639dc71666ca72b5efc22f55da8bf4c7d8f6c30e9cf

Observation ea7b6dc6-ded2-43df-9f8c-b711af13fd8e · outbound

This paper cites R-MADDPG for Partially Observable Environments and Limited Communication.

Towards Fault Tolerance in Multi-Agent Reinforcement Learning R-MADDPG for Partially Observable Environments and Limited Communication

Reference 34

Resolution
unresolved
no resolver link, observed 2026-08-12T05:22:09.623794Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T05:22:09.623794Z digest=sha256:b6ccfcadb0759d3fe89d89c45ca8f30890975d0955bac41311495ce3bb6de82d

Observation bc86a9c6-c263-4a78-9d7f-06f5cf8d9b8c · outbound

This paper cites Multi-agent Deep Reinforcement Learning with Extremely Noisy Observations.

Towards Fault Tolerance in Multi-Agent Reinforcement Learning Multi-agent Deep Reinforcement Learning with Extremely Noisy Observations

Reference 35

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verified exact
local_arxiv, observed 2026-08-12T05:22:09.827043Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T05:22:09.628959Z digest=sha256:ce94b400d5030ca3d73e077210458d29f5266b7f6d25ba16a468f6a6f27ff375

Observation ee732337-c8b8-41d3-8e83-302355453081 · outbound

This paper cites A general survey on attention mechanisms in deep learning,.

Towards Fault Tolerance in Multi-Agent Reinforcement Learning A general survey on attention mechanisms in deep learning,

Reference 36

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verified fuzzy
raw_fallback, observed 2026-08-12T05:22:10.064923Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T05:22:09.634357Z digest=sha256:c20e604791f18c73f136035f3cba5e6546cb0e9207aa8847481413089eeb852d

Observation b1882652-b2bd-4433-ab91-72120af3ce3a · outbound

This paper cites Neural machine translation by jointly learning to align and translate,.

Towards Fault Tolerance in Multi-Agent Reinforcement Learning Neural machine translation by jointly learning to align and translate,

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T05:22:10.049561Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T05:22:09.639520Z digest=sha256:09e056e6b61809bcd1804754b66bc7f1f9e06dac9b7b6935a9e1a2a3dcf4dd9b

Observation 65d3c800-807b-4493-ade8-9bf6cab96d02 · outbound

This paper cites Attention is all you need,.

Towards Fault Tolerance in Multi-Agent Reinforcement Learning Attention is all you need,

Reference 38

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unresolved
no resolver link, observed 2026-08-12T05:22:09.644089Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T05:22:09.644089Z digest=sha256:fbbe258cbdecbdba02490d2a9654e5f45810a8674bd01f50b392bb03e22fe566

Observation 6ba2b20f-7798-412e-a1e8-2f7dad889f7e · outbound

This paper cites Large Language Models: A Survey.

Towards Fault Tolerance in Multi-Agent Reinforcement Learning Large Language Models: A Survey

Reference 39

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unresolved
no resolver link, observed 2026-08-12T05:22:09.649369Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T05:22:09.649369Z digest=sha256:4dcad0c6191d5e86aa70645cf7e6a0bc9176025a05a44e4e8b73152ff4f19144

Observation b2f587ee-4b3e-4c3a-bf79-827809965a3a · outbound

This paper cites Recurrent models of visual attention,.

Towards Fault Tolerance in Multi-Agent Reinforcement Learning Recurrent models of visual attention,

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T05:22:10.024852Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T05:22:09.654574Z digest=sha256:95287d51711bd404e9e2ff1ae8b3e48d38d9ed8b269d45d2454894121872c0b1

Observation c1d66f45-273c-4f6a-98d1-19d5fabc7689 · outbound

This paper cites An image is worth 16x16 words: Transformers for image recognition at scale,.

Towards Fault Tolerance in Multi-Agent Reinforcement Learning An image is worth 16x16 words: Transformers for image recognition at scale,

Reference 41

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unresolved
no resolver link, observed 2026-08-12T05:22:09.659264Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T05:22:09.659264Z digest=sha256:920624904741296988ea482fb06de7618abf35b1a68a80d42161f993fa5db215

Observation 191801cd-6a7f-4a8e-9feb-0ae4f20a7948 · outbound

This paper cites Actor-attention-critic for multi-agent reinforcement learning,.

Towards Fault Tolerance in Multi-Agent Reinforcement Learning Actor-attention-critic for multi-agent reinforcement learning,

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T05:22:09.997264Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T05:22:09.663893Z digest=sha256:a208a59fd40df45ec0ba66ca3cf284f6dd1cba795e9827357974fa17ba31f32e

Observation 1f890d80-f54e-4de1-adc4-a83312ed7550 · outbound

This paper cites Attention-based recurrence for multi-agent rein- forcement learning under stochastic partial observability,.

Towards Fault Tolerance in Multi-Agent Reinforcement Learning Attention-based recurrence for multi-agent rein- forcement learning under stochastic partial observability,

Reference 43

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verified fuzzy
raw_fallback, observed 2026-08-12T05:22:09.982142Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T05:22:09.668955Z digest=sha256:4894103241f47f37fecb7b124f96f7fa658512e1d6530969564423e09876091a

Observation 80f40410-d8c9-4aaa-8315-3f267c55bccf · outbound

This paper cites Attention-Guided Contrastive Role Representations for Multi-Agent Reinforcement Learning.

Towards Fault Tolerance in Multi-Agent Reinforcement Learning Attention-Guided Contrastive Role Representations for Multi-Agent Reinforcement Learning

Reference 44

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unresolved
no resolver link, observed 2026-08-12T05:22:09.673402Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T05:22:09.673402Z digest=sha256:f085614f8c26b44ccf3a3132b7caf6f3e282589340a6d81283d820021afbeaab

Observation c8791e80-f72a-4d9d-bf5b-ae067ef72627 · outbound

This paper cites Novel distributed grus based on hybrid self-attention mechanism for dynamic soft sensing,.

Towards Fault Tolerance in Multi-Agent Reinforcement Learning Novel distributed grus based on hybrid self-attention mechanism for dynamic soft sensing,

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T05:22:09.967218Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T05:22:09.678574Z digest=sha256:0efb8ffed9fa8c5a38ea0673522a836edd759980b61201a4d03f5a26940ee515

Observation 053d63a2-e868-499f-bca3-4a2781eded09 · outbound

This paper cites Learning from noisy labels with distillation,.

Towards Fault Tolerance in Multi-Agent Reinforcement Learning Learning from noisy labels with distillation,

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T05:22:09.952556Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T05:22:09.683251Z digest=sha256:001af4fe959d64c2c84959a1f41758d8534762094e20e48d8e337ed7a339a6bb

Observation e55e301d-8c95-405f-92ef-f574644d8939 · outbound

This paper cites Curriculum reinforcement learning from avoiding collisions to navigating among movable obstacles in diverse environments,.

Towards Fault Tolerance in Multi-Agent Reinforcement Learning Curriculum reinforcement learning from avoiding collisions to navigating among movable obstacles in diverse environments,

Reference 47

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T05:22:09.937232Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T05:22:09.687886Z digest=sha256:b1b0fa80974731d36491b519c3bf19ba68a1c84ceb13323658ae50c6bce942e4

Observation 6abb803a-19f4-4760-9108-5abe83fcfddf · outbound

This paper cites Training region-based object detectors with online hard example mining,.

Towards Fault Tolerance in Multi-Agent Reinforcement Learning Training region-based object detectors with online hard example mining,

Reference 48

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T05:22:09.692721Z digest=sha256:b3339b17d3079936a052f882aa0ac82b30e4af8daaa968bb6a06739ac5e719c6

Observation 25d69e2b-8179-4899-8af9-272d865f768b · outbound

This paper cites Markov games as a framework for multi-agent rein- forcement learning,.

Towards Fault Tolerance in Multi-Agent Reinforcement Learning Markov games as a framework for multi-agent rein- forcement learning,

Reference 49

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T05:22:09.913197Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T05:22:09.697574Z digest=sha256:7b021e487a07377f2a6c6e63a7171da5e6b9f00a1f02d003de1a59d2d2e9a999

Observation 09988ba2-3786-41d3-b537-8435bf29f365 · outbound

This paper cites Planning and acting in partially observable stochastic domains,.

Towards Fault Tolerance in Multi-Agent Reinforcement Learning Planning and acting in partially observable stochastic domains,

Reference 50

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T05:22:09.898186Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T05:22:09.702321Z digest=sha256:15511bf9a2733e8879d46a74cac48fcd05ae6f0a40587e0820b620baf1a38fec

Observation 26dde8a6-cab0-4f40-ac81-0afd5993a78b · outbound

This paper cites Continuous control with deep reinforcement learning,.

Towards Fault Tolerance in Multi-Agent Reinforcement Learning Continuous control with deep reinforcement learning,

Reference 51

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T05:22:09.883105Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T05:22:09.706728Z digest=sha256:c04436882ae88f4e7a083214c39786b4680871770c2e84216643cfd14a14a402

Observation 6818dc0f-6a56-4ce8-8f52-27eb888893bb · outbound

This paper cites RLlib: Abstractions for Distributed Reinforcement Learning.

Towards Fault Tolerance in Multi-Agent Reinforcement Learning RLlib: Abstractions for Distributed Reinforcement Learning

Reference 52

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unresolved
no resolver link, observed 2026-08-12T05:22:09.711386Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T05:22:09.711386Z digest=sha256:edd0743040eb3a84cf6f372d385cd5db905b65139eac77da8ec6475c9ad352b9

Observation 5130b66b-b7bf-41fc-a2e8-19851e9801e3 · outbound

This paper cites rlpyt: A Research Code Base for Deep Reinforcement Learning in PyTorch.

Towards Fault Tolerance in Multi-Agent Reinforcement Learning rlpyt: A Research Code Base for Deep Reinforcement Learning in PyTorch

Reference 53

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unresolved
no resolver link, observed 2026-08-12T05:22:09.716785Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T05:22:09.716785Z digest=sha256:3172665d7e4435d0ccff916f97629980a1df8dcad8a0c3c96827d067155728b2

Observation 0769e7bb-b4a0-4c92-8473-4d708b32184f · outbound

This paper cites Experiment tracking with weights and biases,.

Towards Fault Tolerance in Multi-Agent Reinforcement Learning Experiment tracking with weights and biases,

Reference 54

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unresolved
no resolver link, observed 2026-08-12T05:22:09.722683Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T05:22:09.722683Z digest=sha256:7bc70aa57eaf8704cd318a37f9da13fc8023c0eef02c97c7177b1d7a3b94c37b

Pith citing papers

Observation ac8229a5-852d-41d9-ad84-737d7149333d · inbound

Exploring Critical Testing Scenarios for Decision-Making Policies: An LLM Approach cites this paper.

Exploring Critical Testing Scenarios for Decision-Making Policies: An LLM Approach Towards Fault Tolerance in Multi-Agent Reinforcement Learning

Reference 10

Resolution
verified exact
local_arxiv, observed 2026-08-11T19:28:52.644424Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T19:28:52.274750Z digest=sha256:d8ac21ff81b261086163cffc3ae18813a651356931c012e9eb12541dd04556c8