Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-01T15:07:45.671697Z
Paper Citation Record · LEDGER
As of 9 August 2026, this Paper Citation Record lists 34 of 34 outbound references and 0 inbound Pith citation observations for arXiv:2607.18554.
A citation records a reference. It does not transfer a finding from one paper to another.
Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-01T15:07:45.671697Z
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+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
34 of 34 outbound references displayed
External citation measurements
No source-named external measurement is stored.
Observation db074900-9149-4f9e-9ff3-efba1aaf7f94 · outbound
Scalable Policy Optimization for Networked Multi-Agent Reinforcement Learning with Continuous State-Action Spaces Multi-agent reinforcement learning: A selective overview of theories and algorithms,
Reference 1
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Observation 4d0640fd-9165-401e-a914-f4e93c376b17 · outbound
Scalable Policy Optimization for Networked Multi-Agent Reinforcement Learning with Continuous State-Action Spaces Stability constrained reinforcement learning for decentralized real-time voltage control,
Reference 2
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Observation 98d8d835-e27f-40d5-9f1e-f373ce4ec53b · outbound
Scalable Policy Optimization for Networked Multi-Agent Reinforcement Learning with Continuous State-Action Spaces Scalable reinforcement learning of localized policies for multi-agent networked systems,
Reference 3
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Observation 39abe093-75f4-43d7-bcd1-2ed504277f55 · outbound
Scalable Policy Optimization for Networked Multi-Agent Reinforcement Learning with Continuous State-Action Spaces Scalable reinforcement learning for multiagent networked sys- tems,
Reference 4
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Observation 9b0bebf4-cc51-4cac-8dcc-91045062ae00 · outbound
Scalable Policy Optimization for Networked Multi-Agent Reinforcement Learning with Continuous State-Action Spaces Multi-agent reinforcement learning in stochastic networked systems,
Reference 5
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Observation 26d11f3e-74f4-45f7-b721-4fbdb9af915f · outbound
Scalable Policy Optimization for Networked Multi-Agent Reinforcement Learning with Continuous State-Action Spaces Global convergence of localized policy iteration in networked multi-agent reinforcement learning,
Reference 6
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Observation a5fd7b01-b3ea-470a-983e-9fb19504aaac · outbound
Scalable Policy Optimization for Networked Multi-Agent Reinforcement Learning with Continuous State-Action Spaces Fully decentralized multi-agent reinforcement learning with networked agents,
Reference 7
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Observation 380f43a1-8d15-4e7e-a29a-f779e003e649 · outbound
Scalable Policy Optimization for Networked Multi-Agent Reinforcement Learning with Continuous State-Action Spaces Finite-time analysis of dis- tributed td (0) with linear function approximation on multi-agent rein- forcement learning,
Reference 8
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Observation 80fce664-357f-49ca-ac5e-0e9859a7e637 · outbound
Scalable Policy Optimization for Networked Multi-Agent Reinforcement Learning with Continuous State-Action Spaces Decentralized online convex optimization in networked systems,
Reference 9
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Observation ff360be4-3fe0-43b1-a6e6-7082e426bde6 · outbound
Scalable Policy Optimization for Networked Multi-Agent Reinforcement Learning with Continuous State-Action Spaces Multi-agent Reinforcement Learning for Networked System Control
Reference 10
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Observation ff10ef29-0db5-445e-b3dd-d0e651b15034 · outbound
Scalable Policy Optimization for Networked Multi-Agent Reinforcement Learning with Continuous State-Action Spaces Random features for large-scale kernel machines,
Reference 11
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Observation 45b6321f-8983-43f4-8e68-0aa60bec6e0b · outbound
Scalable Policy Optimization for Networked Multi-Agent Reinforcement Learning with Continuous State-Action Spaces Random features for ker- nel approximation: A survey on algorithms, theory, and beyond,
Reference 12
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Observation 0758319c-58e1-4bb2-ae17-8568332d969e · outbound
Scalable Policy Optimization for Networked Multi-Agent Reinforcement Learning with Continuous State-Action Spaces Scalable spectral representations for multi-agent reinforcement learning in network MDPs
Reference 13
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Observation af3c1833-2880-4063-92fd-cfa01126defc · outbound
Scalable Policy Optimization for Networked Multi-Agent Reinforcement Learning with Continuous State-Action Spaces Linear least-squares algorithms for temporal difference learning,
Reference 14
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Observation 820e9772-ffdf-4788-abfe-c477b2a7d58c · outbound
Scalable Policy Optimization for Networked Multi-Agent Reinforcement Learning with Continuous State-Action Spaces Technical update: Least-squares temporal difference learning,
Reference 15
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Observation 9c84af58-9a4d-4e35-8308-b8ae758afa4d · outbound
Scalable Policy Optimization for Networked Multi-Agent Reinforcement Learning with Continuous State-Action Spaces Finite-sample analysis of least-squares policy iteration,
Reference 16
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Observation bf0b56bf-d20b-4ebe-8711-5e4887d30db3 · outbound
Scalable Policy Optimization for Networked Multi-Agent Reinforcement Learning with Continuous State-Action Spaces A finite time analysis of temporal difference learning with linear function approximation,
Reference 17
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Observation fc623c16-c7c9-42c7-b88d-12c72fc7b6bf · outbound
Scalable Policy Optimization for Networked Multi-Agent Reinforcement Learning with Continuous State-Action Spaces An introduction to matrix concentration inequalities,
Reference 18
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Observation 8a738021-d68d-4b74-aa2f-2c70b18a138e · outbound
Scalable Policy Optimization for Networked Multi-Agent Reinforcement Learning with Continuous State-Action Spaces Vershynin,High-dimensional probability: An introduction with ap- plications in data science
Reference 19
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Observation 9912ec25-11ff-43f0-b13c-6c977f9c24e1 · outbound
Scalable Policy Optimization for Networked Multi-Agent Reinforcement Learning with Continuous State-Action Spaces Optimum bounds for the distributions of martingales in banach spaces,
Reference 20
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Observation 02ba7837-d616-490e-971e-35b18668207b · outbound
Scalable Policy Optimization for Networked Multi-Agent Reinforcement Learning with Continuous State-Action Spaces Unresolved cited work
Reference 21
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Observation 7dd116c8-5efc-4694-ad44-d110294d6535 · outbound
Scalable Policy Optimization for Networked Multi-Agent Reinforcement Learning with Continuous State-Action Spaces Policy gradi- ent methods for reinforcement learning with function approximation,
Reference 22
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Observation f6d9d64c-2c40-4010-a9b0-ec8248b72543 · outbound
Scalable Policy Optimization for Networked Multi-Agent Reinforcement Learning with Continuous State-Action Spaces Lower bounds for non-convex stochastic optimization,
Reference 23
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Observation fdf0f9db-840d-4561-9b15-0bcb01c2a9bd · outbound
Scalable Policy Optimization for Networked Multi-Agent Reinforcement Learning with Continuous State-Action Spaces On the theory of policy gradient methods: Optimality, approximation, and distribution shift,
Reference 24
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Observation 17da105f-f405-4446-bce6-e922b25e8719 · outbound
Scalable Policy Optimization for Networked Multi-Agent Reinforcement Learning with Continuous State-Action Spaces Multi-agent actor-critic for mixed cooperative-competitive environ- ments,
Reference 25
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Observation 1ac1c5a9-6649-41a3-8111-6a498f25c506 · outbound
Scalable Policy Optimization for Networked Multi-Agent Reinforcement Learning with Continuous State-Action Spaces Counterfactual multi-agent policy gradients,
Reference 26
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Observation 473a9151-3d76-4f46-a4b3-2dbc7785c245 · outbound
Scalable Policy Optimization for Networked Multi-Agent Reinforcement Learning with Continuous State-Action Spaces Actor-attention-critic for multi-agent reinforcement learning,
Reference 27
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Observation b786be04-134f-4043-9584-1e38448ba09a · outbound
Scalable Policy Optimization for Networked Multi-Agent Reinforcement Learning with Continuous State-Action Spaces The surprising effectiveness of ppo in cooperative multi-agent games,
Reference 28
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Observation a368e43b-aa00-4bd3-bb82-6b0019cdf913 · outbound
Scalable Policy Optimization for Networked Multi-Agent Reinforcement Learning with Continuous State-Action Spaces Near-optimal distributed linear-quadratic regulator for networked systems,
Reference 29
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Observation 11be9423-6ad9-4224-baf8-250cb9548d5b · outbound
Scalable Policy Optimization for Networked Multi-Agent Reinforcement Learning with Continuous State-Action Spaces Network reconfiguration in distribution systems for loss reduction and load balancing,
Reference 30
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Observation 67f10cbf-2aa6-477b-9827-8c23e0c86bdc · outbound
Scalable Policy Optimization for Networked Multi-Agent Reinforcement Learning with Continuous State-Action Spaces The description of a random field by means of conditional probabilities and conditions of its regularity,
Reference 31
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Observation cb46e0a5-6875-44b3-ba4b-4267b90c209c · outbound
Scalable Policy Optimization for Networked Multi-Agent Reinforcement Learning with Continuous State-Action Spaces Can local particle filters beat the curse of dimensionality?
Reference 32
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Observation dde6708c-d21c-42d2-afc5-192f05407b19 · outbound
Scalable Policy Optimization for Networked Multi-Agent Reinforcement Learning with Continuous State-Action Spaces Mini-batch stochastic approx- imation methods for nonconvex stochastic composite optimization,
Reference 33
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Observation e0bcf005-760d-4027-9f54-2ae8649d016b · outbound
Scalable Policy Optimization for Networked Multi-Agent Reinforcement Learning with Continuous State-Action Spaces Stochastic first-and zeroth-order methods for nonconvex stochastic programming,
Reference 34
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No inbound Pith citation observations are available.