Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-05-08T04:21:21.394989Z
Paper Citation Record · LEDGER
Independent Learning of Nash Equilibria in Partially Observable Markov Potential Games with Decoupled Dynamics
As of 14 August 2026, this Paper Citation Record lists 49 of 49 outbound references and 2 inbound Pith citation observations for arXiv:2605.06377.
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 2026-08-14T06:32:32.682623+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-07-14T18:08:39.286851Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-07-03T03:07:35.876213Z
Reference resolution
49 of 49 outbound references displayed
- verified exact1
- verified fuzzy48
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 69a48fe5-c4bc-4ddf-a9f2-83ce4e00e05f · outbound
This paper cites Partially observable Markov decision processes in robotics: A survey.IEEE Transactions on Robotics, 39(1):21–40.
Independent Learning of Nash Equilibria in Partially Observable Markov Potential Games with Decoupled Dynamics Partially observable Markov decision processes in robotics: A survey.IEEE Transactions on Robotics, 39(1):21–40
Reference 1
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T21:08:03.355878Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 71cc4f90-17cb-4909-9575-9f7db2bfedf0 · outbound
This paper cites Intention-aware online POMDP planning for autonomous driving in a crowd.
Independent Learning of Nash Equilibria in Partially Observable Markov Potential Games with Decoupled Dynamics Intention-aware online POMDP planning for autonomous driving in a crowd
Reference 2
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T21:08:03.352540Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 60dc3f7c-de65-402b-88d7-9b7b0b5f5f59 · outbound
This paper cites Asynchronous multi-agent deep reinforcement learning under partial observability.The International Journal of Robotics Research, 44(8):1257–1286.
Independent Learning of Nash Equilibria in Partially Observable Markov Potential Games with Decoupled Dynamics Asynchronous multi-agent deep reinforcement learning under partial observability.The International Journal of Robotics Research, 44(8):1257–1286
Reference 3
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T21:08:03.349329Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation e91fb2d8-b0bd-4870-940c-154ed73709fc · outbound
This paper cites Solving imperfect information Poker games using Monte Carlo search and POMDP models.
Independent Learning of Nash Equilibria in Partially Observable Markov Potential Games with Decoupled Dynamics Solving imperfect information Poker games using Monte Carlo search and POMDP models
Reference 4
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T21:08:03.359460Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 718c7368-0bf2-4899-95ea-d4b2004c72a4 · outbound
This paper cites Markov decision processes: Discrete stochastic dynamic programming.
Independent Learning of Nash Equilibria in Partially Observable Markov Potential Games with Decoupled Dynamics Markov decision processes: Discrete stochastic dynamic programming
Reference 5
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T21:08:03.200973Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 216901d5-1719-4ad0-91a6-474726f676c2 · outbound
This paper cites The complexity of Markov decision processes.
Independent Learning of Nash Equilibria in Partially Observable Markov Potential Games with Decoupled Dynamics The complexity of Markov decision processes
Reference 6
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T21:08:03.328980Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 19e2eb72-d70b-4238-9c14-69738723d874 · outbound
This paper cites PAC reinforcement learning with rich observations.Advances in Neural Information Processing Systems.
Independent Learning of Nash Equilibria in Partially Observable Markov Potential Games with Decoupled Dynamics PAC reinforcement learning with rich observations.Advances in Neural Information Processing Systems
Reference 7
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T21:08:03.308286Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 6587cb37-08d3-4b53-be0f-bbb3ba89c05e · outbound
This paper cites Planning and learning in partially observ- able systems via filter stability.
Independent Learning of Nash Equilibria in Partially Observable Markov Potential Games with Decoupled Dynamics Planning and learning in partially observ- able systems via filter stability
Reference 8
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T21:08:03.274462Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation db418b22-91ae-40ab-b033-e87ee77dd27e · outbound
This paper cites When is partially observable reinforcement learning not scary? InConference on Learning Theory, pages 5175–5220.
Independent Learning of Nash Equilibria in Partially Observable Markov Potential Games with Decoupled Dynamics When is partially observable reinforcement learning not scary? InConference on Learning Theory, pages 5175–5220
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T21:08:03.249594Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 97f5c070-e5fd-4db9-8d85-9296d556f8bb · outbound
This paper cites Learning in observable POMDPs, without computationally intractable oracles.Advances in Neural Information Processing Systems.
Independent Learning of Nash Equilibria in Partially Observable Markov Potential Games with Decoupled Dynamics Learning in observable POMDPs, without computationally intractable oracles.Advances in Neural Information Processing Systems
Reference 10
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T21:08:03.252575Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation e5c5ed3f-a62b-4437-b0f7-ee9ca53c3633 · outbound
This paper cites Convergence of finite memory Q-learning for POMDPs and near optimality of learned policies under filter stability.Mathematics of Operations Research, 48(4):2066–2093.
Independent Learning of Nash Equilibria in Partially Observable Markov Potential Games with Decoupled Dynamics Convergence of finite memory Q-learning for POMDPs and near optimality of learned policies under filter stability.Mathematics of Operations Research, 48(4):2066–2093
Reference 11
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T21:08:03.271183Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 630d03f8-96af-4ee4-9748-7a8efa884a78 · outbound
This paper cites Finite-time analysis of natural actor-critic for POMDPs.
Independent Learning of Nash Equilibria in Partially Observable Markov Potential Games with Decoupled Dynamics Finite-time analysis of natural actor-critic for POMDPs
Reference 12
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T21:08:03.230804Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 79725dd4-9768-41b9-937b-c2eacbf48c3f · outbound
This paper cites Scalable policy-based RL algorithms for POMDPs.Advances in Neural Information Processing Systems.
Independent Learning of Nash Equilibria in Partially Observable Markov Potential Games with Decoupled Dynamics Scalable policy-based RL algorithms for POMDPs.Advances in Neural Information Processing Systems
Reference 13
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T21:08:03.234750Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 61de9c0a-7437-42da-9523-1afae936d18a · outbound
This paper cites Model-based learning of near-optimal finite-window policies in POMDPs.
Independent Learning of Nash Equilibria in Partially Observable Markov Potential Games with Decoupled Dynamics Model-based learning of near-optimal finite-window policies in POMDPs
Reference 14
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T21:46:43.629055Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 2d54477b-ed41-4314-b4b7-9b2741d369c2 · outbound
This paper cites Approxi- mate solutions for partially observable stochastic games with common payoffs.
Independent Learning of Nash Equilibria in Partially Observable Markov Potential Games with Decoupled Dynamics Approxi- mate solutions for partially observable stochastic games with common payoffs
Reference 15
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T21:08:03.238445Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 6c511b5e-da33-4da1-9ae0-51d37c631972 · outbound
This paper cites IEEE.
Reference 16
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T21:08:03.241904Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 9e1ac18e-da63-4668-b46d-ba88e23012e6 · outbound
This paper cites Deep decentralized multi-task multi-agent reinforcement learning under partial observability.
Independent Learning of Nash Equilibria in Partially Observable Markov Potential Games with Decoupled Dynamics Deep decentralized multi-task multi-agent reinforcement learning under partial observability
Reference 17
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T21:08:03.280618Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 3f86a19f-8d39-42f1-b2c9-e337843aa0ae · outbound
This paper cites Improving policies via search in cooperative partially observable games.
Independent Learning of Nash Equilibria in Partially Observable Markov Potential Games with Decoupled Dynamics Improving policies via search in cooperative partially observable games
Reference 18
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T21:08:03.293501Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation d6907efa-6d22-4145-baf3-51cef0396577 · outbound
This paper cites Divergence-regularized discounted aggregation: Equilibrium finding in multiplayer partially observable stochastic games.
Independent Learning of Nash Equilibria in Partially Observable Markov Potential Games with Decoupled Dynamics Divergence-regularized discounted aggregation: Equilibrium finding in multiplayer partially observable stochastic games
Reference 19
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T21:08:03.217431Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 7fee00d0-be4c-4023-9da1-fa22cda71002 · outbound
This paper cites Sample-efficient reinforcement learning of partially observable Markov games.Advances in Neural Information Processing Systems.
Independent Learning of Nash Equilibria in Partially Observable Markov Potential Games with Decoupled Dynamics Sample-efficient reinforcement learning of partially observable Markov games.Advances in Neural Information Processing Systems
Reference 20
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T21:08:03.223240Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation e2d44f1b-8f73-4a26-82ff-fe0f7f13826d · outbound
This paper cites The complexity of computing a Nash equilibrium.Communications of the ACM, 52(2):89–97.
Independent Learning of Nash Equilibria in Partially Observable Markov Potential Games with Decoupled Dynamics The complexity of computing a Nash equilibrium.Communications of the ACM, 52(2):89–97
Reference 21
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T21:08:03.204272Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 49985356-1745-4260-a416-d02755310584 · outbound
This paper cites Learning parametric closed-loop policies for Markov potential games.
Independent Learning of Nash Equilibria in Partially Observable Markov Potential Games with Decoupled Dynamics Learning parametric closed-loop policies for Markov potential games
Reference 22
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T21:08:03.207487Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation a922cc57-9e25-4787-a76d-09ab7f945ad9 · outbound
This paper cites When can we learn general-sum Markov games with a large number of players sample-efficiently? InInternational Conference on Learning Representations.
Independent Learning of Nash Equilibria in Partially Observable Markov Potential Games with Decoupled Dynamics When can we learn general-sum Markov games with a large number of players sample-efficiently? InInternational Conference on Learning Representations
Reference 23
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T21:08:03.196282Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation a1130373-812e-447b-84fe-949512bfba3e · outbound
This paper cites Global conver- gence of multi-agent policy gradient in Markov potential games.
Independent Learning of Nash Equilibria in Partially Observable Markov Potential Games with Decoupled Dynamics Global conver- gence of multi-agent policy gradient in Markov potential games
Reference 24
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T21:08:03.214346Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 4a91d1b2-4bc6-4422-8dab-2f17d885407a · outbound
This paper cites Independent policy gradient for large-scale Markov potential games: Sharper rates, function approximation, and game-agnostic convergence.
Independent Learning of Nash Equilibria in Partially Observable Markov Potential Games with Decoupled Dynamics Independent policy gradient for large-scale Markov potential games: Sharper rates, function approximation, and game-agnostic convergence
Reference 25
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T21:08:03.220442Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 56ffb599-33aa-47b1-8122-7fc53dd71917 · outbound
This paper cites Independent and decentralized learning in Markov potential games.IEEE Transactions on Automatic Control.
Independent Learning of Nash Equilibria in Partially Observable Markov Potential Games with Decoupled Dynamics Independent and decentralized learning in Markov potential games.IEEE Transactions on Automatic Control
Reference 26
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T21:08:03.346062Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 6bd9cd5b-f191-4ade-88e9-b48a0e1d43ff · outbound
This paper cites The complexity of decentralized control of Markov decision processes.Mathematics of operations research, 27(4): 819–840.
Independent Learning of Nash Equilibria in Partially Observable Markov Potential Games with Decoupled Dynamics The complexity of decentralized control of Markov decision processes.Mathematics of operations research, 27(4): 819–840
Reference 27
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T21:08:03.210456Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 3ab58e87-d03d-44a5-94a4-c02db799cb7c · outbound
This paper cites Optimally solving Dec-POMDPs as continuous-state MDPs.Journal of Artificial Intelligence Research, 55:443–497.
Independent Learning of Nash Equilibria in Partially Observable Markov Potential Games with Decoupled Dynamics Optimally solving Dec-POMDPs as continuous-state MDPs.Journal of Artificial Intelligence Research, 55:443–497
Reference 28
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T21:08:03.226614Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 33181333-6837-463f-8318-8ce7afc662fb · outbound
This paper cites Learning to act in decentralized partially observable MDPs.
Independent Learning of Nash Equilibria in Partially Observable Markov Potential Games with Decoupled Dynamics Learning to act in decentralized partially observable MDPs
Reference 29
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T21:08:03.290221Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation da020291-180d-4c92-b5be-a41e0be5fc47 · outbound
This paper cites Decentralized learning of finite-memory policies in Dec-POMDPs.IFAC-PapersOnLine, 56(2):2601–2607.
Independent Learning of Nash Equilibria in Partially Observable Markov Potential Games with Decoupled Dynamics Decentralized learning of finite-memory policies in Dec-POMDPs.IFAC-PapersOnLine, 56(2):2601–2607
Reference 30
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T21:08:03.342616Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 8defbd85-8b5a-4554-a9ef-02bcfdafaeb4 · outbound
This paper cites Partially observable multi-agent rl with (quasi-) efficiency: The blessing of information sharing.
Independent Learning of Nash Equilibria in Partially Observable Markov Potential Games with Decoupled Dynamics Partially observable multi-agent rl with (quasi-) efficiency: The blessing of information sharing
Reference 31
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T21:08:03.335667Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation de4ce7a4-662f-4c0e-94d4-f5b036a44900 · outbound
This paper cites Constrained stochastic games in wireless networks.
Independent Learning of Nash Equilibria in Partially Observable Markov Potential Games with Decoupled Dynamics Constrained stochastic games in wireless networks
Reference 32
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T21:08:03.338984Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 345683cb-5f3e-45aa-809f-b6f6f079532d · outbound
This paper cites Dynamic discrete power control in cellular networks.IEEE Transactions on Automatic Control, 54(10):2328–2340.
Independent Learning of Nash Equilibria in Partially Observable Markov Potential Games with Decoupled Dynamics Dynamic discrete power control in cellular networks.IEEE Transactions on Automatic Control, 54(10):2328–2340
Reference 33
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T21:08:03.318304Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 78d9832a-357d-4e45-ae6c-9c69f690b7f8 · outbound
This paper cites Stochastic games for the smart grid energy management with prospect prosumers.IEEE Transactions on Automatic Control, 63(8):2327–2342.
Independent Learning of Nash Equilibria in Partially Observable Markov Potential Games with Decoupled Dynamics Stochastic games for the smart grid energy management with prospect prosumers.IEEE Transactions on Automatic Control, 63(8):2327–2342
Reference 34
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T21:08:03.322138Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 9fe805af-d92f-47cb-af69-a16a975aa1dd · outbound
This paper cites Markov games with decoupled dynamics: Price of anarchy and sample complexity.
Independent Learning of Nash Equilibria in Partially Observable Markov Potential Games with Decoupled Dynamics Markov games with decoupled dynamics: Price of anarchy and sample complexity
Reference 35
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T21:08:03.311731Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation b67fce2c-8b0f-47dc-acda-f7eeb144b1c8 · outbound
This paper cites Learning stationary nash equilibrium policies in n-player stochastic games with independent chains.SIAM Journal on Control and Optimization, 62(2):799–825.
Independent Learning of Nash Equilibria in Partially Observable Markov Potential Games with Decoupled Dynamics Learning stationary nash equilibrium policies in n-player stochastic games with independent chains.SIAM Journal on Control and Optimization, 62(2):799–825
Reference 36
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T21:08:03.314917Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation e8c3a4c7-7919-48f6-9b2f-ad466bff21f1 · outbound
This paper cites Markov α-potential games.IEEE Transactions on Automatic Control.
Independent Learning of Nash Equilibria in Partially Observable Markov Potential Games with Decoupled Dynamics Markov α-potential games.IEEE Transactions on Automatic Control
Reference 37
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T21:08:03.300107Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation bbb1df95-7d41-433c-8d84-892c3e1566c2 · outbound
This paper cites An α-potential game framework for n-player dynamic games.SIAM Journal on Control and Optimization, 63(4):2964–3005.
Independent Learning of Nash Equilibria in Partially Observable Markov Potential Games with Decoupled Dynamics An α-potential game framework for n-player dynamic games.SIAM Journal on Control and Optimization, 63(4):2964–3005
Reference 38
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T21:08:03.303621Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation aa54e1d0-6210-491f-adf6-b84677923844 · outbound
This paper cites On the global convergence rates of decentralized softmax gradient play in Markov potential games.Advances in Neural Information Processing Systems.
Independent Learning of Nash Equilibria in Partially Observable Markov Potential Games with Decoupled Dynamics On the global convergence rates of decentralized softmax gradient play in Markov potential games.Advances in Neural Information Processing Systems
Reference 39
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T21:08:03.325810Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation dae29f47-cec7-42ad-a76c-443f99e7428b · outbound
This paper cites Multi-agent learning via Markov potential games in marketplaces for distributed energy resources.
Independent Learning of Nash Equilibria in Partially Observable Markov Potential Games with Decoupled Dynamics Multi-agent learning via Markov potential games in marketplaces for distributed energy resources
Reference 40
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T21:08:03.332452Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 6a1e3599-0046-4943-bb08-e1625495e414 · outbound
This paper cites Hidden Markov models.Unpublished lecture notes.
Independent Learning of Nash Equilibria in Partially Observable Markov Potential Games with Decoupled Dynamics Hidden Markov models.Unpublished lecture notes
Reference 41
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T21:08:03.283642Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 593d4d7e-5fdc-4dbc-ad35-1f5697fddc83 · outbound
This paper cites Near optimality of finite memory feedback policies in partially observed Markov decision processes.Journal of Machine Learning Research, 23(11):1–46.
Independent Learning of Nash Equilibria in Partially Observable Markov Potential Games with Decoupled Dynamics Near optimality of finite memory feedback policies in partially observed Markov decision processes.Journal of Machine Learning Research, 23(11):1–46
Reference 42
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T21:08:03.296997Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation ad83b200-55d3-4e67-8760-02942512fe24 · outbound
This paper cites Multi-agent reinforcement learning: A selective overview of theories and algorithms.Handbook of reinforcement learning and control, pages 321–384.
Independent Learning of Nash Equilibria in Partially Observable Markov Potential Games with Decoupled Dynamics Multi-agent reinforcement learning: A selective overview of theories and algorithms.Handbook of reinforcement learning and control, pages 321–384
Reference 43
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T21:08:03.264407Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 091b3b8c-6cb5-4010-91ef-cb930369dba9 · outbound
This paper cites Independent policy gradient methods for competitive reinforcement learning.Advances in Neural Information Processing Systems.
Independent Learning of Nash Equilibria in Partially Observable Markov Potential Games with Decoupled Dynamics Independent policy gradient methods for competitive reinforcement learning.Advances in Neural Information Processing Systems
Reference 44
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T21:08:03.267895Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 98e7f92a-bcaa-4822-a682-e53caa3dc0cd · outbound
This paper cites Provable self-play algorithms for competitive reinforcement learning.
Independent Learning of Nash Equilibria in Partially Observable Markov Potential Games with Decoupled Dynamics Provable self-play algorithms for competitive reinforcement learning
Reference 45
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T21:08:03.277401Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 9e2c2821-9532-44f6-bebb-577fdc4ef108 · outbound
This paper cites Cyclic equilibria in Markov games.
Independent Learning of Nash Equilibria in Partially Observable Markov Potential Games with Decoupled Dynamics Cyclic equilibria in Markov games
Reference 46
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T21:08:03.255859Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation c81aaa2d-25d0-4ddd-972f-4ee1ebb6015b · outbound
This paper cites On the sample complexity of reinforcement learning with a generative model.
Independent Learning of Nash Equilibria in Partially Observable Markov Potential Games with Decoupled Dynamics On the sample complexity of reinforcement learning with a generative model
Reference 47
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T21:08:03.260428Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 70a1808d-fd33-49bf-8a13-c67e67116b09 · outbound
This paper cites Near-optimal reinforcement learning in polynomial time.
Independent Learning of Nash Equilibria in Partially Observable Markov Potential Games with Decoupled Dynamics Near-optimal reinforcement learning in polynomial time
Reference 48
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T21:08:03.286792Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation fe969516-8ebf-4c36-b2d3-498f585aa89e · outbound
This paper cites HX h′=h rm i,h′(sh′, ah′)|(a 1, o1, . . . , ah−1, oh−1) =τ # V m i,h(π;w) :=E π, s1∼µ.
Independent Learning of Nash Equilibria in Partially Observable Markov Potential Games with Decoupled Dynamics HX h′=h rm i,h′(sh′, ah′)|(a 1, o1, . . . , ah−1, oh−1) =τ # V m i,h(π;w) :=E π, s1∼µ
Reference 49
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T21:08:03.246122Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Pith citing papers
Observation e8160e90-27bb-4425-82c9-21cbb7555b3f · inbound
Limit Theory for $N$-Player $\alpha$-Potential Games cites this paper.
Limit Theory for $N$-Player $\alpha$-Potential Games Independent Learning of Nash Equilibria in Partially Observable Markov Potential Games with Decoupled Dynamics
Reference 7
- Resolution
- verified exact
- local_arxiv, observed 2026-07-03T03:07:35.877876Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation e528b307-8d9e-47c6-8855-d18033a277e6 · inbound
Limit Theory for $N$-Player $\alpha$-Potential Games cites this paper.
Limit Theory for $N$-Player $\alpha$-Potential Games Independent Learning of Nash Equilibria in Partially Observable Markov Potential Games with Decoupled Dynamics
Reference 7
- Resolution
- unresolved
- no resolver link, observed 2026-07-14T18:08:39.286851Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.