{"as_of":"2026-08-14T15:11:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:4e4962754dc58405d16b03cebe0eee9472be83e30ab15979b7060cd7a2279060","coverage":[{"denominator":49,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":49,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-08T04:21:21.394989Z","state":"measured"},{"denominator":51,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":51,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-14T06:32:32.682623+00:00","state":"measured"},{"denominator":2,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":2,"source":"paper_references, paper_reference_links","source_observed_at":"2026-07-14T18:08:39.286851Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-07-03T03:07:35.876213Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2605.06377","last_updated":"2026-05-07T14:56:56Z","snapshot_observed_at":"2026-07-06T23:18:51.157048Z","submitted_at":"2026-05-07T14:56:56Z","title":"Independent Learning of Nash Equilibria in Partially Observable Markov Potential Games with Decoupled Dynamics","version":1},"cited_work":{"arxiv_id":"2605.06377","doi":null,"metadata_source":"pith","pith_arxiv_id":"2605.06377","snapshot_observed_at":"2026-07-03T03:07:35.876213Z","title":"Independent Learning of Nash Equilibria in Partially Observable Markov Potential Games with Decoupled Dynamics","venue":"cs.GT","work_id":"fa6b45bd-b67d-4cee-afcd-94d5a14ef365","year":2026},"citing_paper":{"arxiv_id":"2606.09815","last_updated":"2026-07-13T10:18:34Z","snapshot_observed_at":"2026-08-08T12:26:47.318036Z","submitted_at":"2026-06-08T17:55:47Z","title":"Limit Theory for $N$-Player $\\alpha$-Potential Games","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-06-27T15:26:43.020134Z"},"links":{"cited_paper":"/paper/2605.06377","citing_paper":"/paper/2606.09815"},"observation_digest":"sha256:7ae2ebbf82beab14f34ac872dd61f4e3cd9806bac3ffb8166e26c3b158447978","observation_id":"e8160e90-27bb-4425-82c9-21cbb7555b3f","resolution":{"observed_at":"2026-07-03T03:07:35.877876Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2605.06377","last_updated":"2026-05-07T14:56:56Z","snapshot_observed_at":"2026-07-06T23:18:51.157048Z","submitted_at":"2026-05-07T14:56:56Z","title":"Independent Learning of Nash Equilibria in Partially Observable Markov Potential Games with Decoupled Dynamics","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2605.06377","snapshot_observed_at":"2026-07-14T18:08:39.286851Z","title":"Independent learning of nash equilibria in partially observable markov potential games with decoupled dynamics.arXiv preprint arXiv:2605.06377, 2026","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2606.09815","last_updated":"2026-07-13T10:18:34Z","snapshot_observed_at":"2026-08-08T12:26:47.318036Z","submitted_at":"2026-06-08T17:55:47Z","title":"Limit Theory for $N$-Player $\\alpha$-Potential Games","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-07-14T18:08:39.286851Z"},"links":{"cited_paper":"/paper/2605.06377","citing_paper":"/paper/2606.09815"},"observation_digest":"sha256:382d773f9ec88803da847e7856748857a71de8a3b13b72e07e570a32a4242a51","observation_id":"e528b307-8d9e-47c6-8855-d18033a277e6","resolution":{"observed_at":"2026-07-14T18:08:39.286851Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2605.06377/citation-record","integrity":"/paper/2605.06377/integrity","json":"/paper/2605.06377/citation-record.json","paper":"/paper/2605.06377"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Partially observable Markov decision processes in robotics: A survey.IEEE Transactions on Robotics, 39(1):21–40","venue":null,"work_id":"9c90e685-d295-4629-b2ef-3ab6517f8ea9","year":2022},"citing_paper":{"arxiv_id":"2605.06377","last_updated":"2026-05-07T14:56:56Z","snapshot_observed_at":"2026-07-06T23:18:51.157048Z","submitted_at":"2026-05-07T14:56:56Z","title":"Independent Learning of Nash Equilibria in Partially Observable Markov Potential Games with Decoupled Dynamics","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-05-08T04:21:21.394989Z"},"links":{"citing_paper":"/paper/2605.06377"},"observation_digest":"sha256:6000c901d88c934d67d4026a4d5df2240bd89995ca07ab2735620df1d6771b77","observation_id":"69a48fe5-c4bc-4ddf-a9f2-83ce4e00e05f","resolution":{"observed_at":"2026-05-26T21:08:03.355878Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Intention-aware online POMDP planning for autonomous driving in a crowd","venue":null,"work_id":"4026cbf3-91e6-4586-9e3d-9bf469ae6111","year":2015},"citing_paper":{"arxiv_id":"2605.06377","last_updated":"2026-05-07T14:56:56Z","snapshot_observed_at":"2026-07-06T23:18:51.157048Z","submitted_at":"2026-05-07T14:56:56Z","title":"Independent Learning of Nash Equilibria in Partially Observable Markov Potential Games with Decoupled Dynamics","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-05-08T04:21:21.394989Z"},"links":{"citing_paper":"/paper/2605.06377"},"observation_digest":"sha256:726c7203168fed9f1e9a1bb1b09a764e89b4fa51fa2703e4cde96e2053b2f253","observation_id":"71cc4f90-17cb-4909-9575-9f7db2bfedf0","resolution":{"observed_at":"2026-05-26T21:08:03.352540Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Asynchronous multi-agent deep reinforcement learning under partial observability.The International Journal of Robotics Research, 44(8):1257–1286","venue":null,"work_id":"13aaacae-ee82-4b22-a7b0-b61035483bc7","year":2025},"citing_paper":{"arxiv_id":"2605.06377","last_updated":"2026-05-07T14:56:56Z","snapshot_observed_at":"2026-07-06T23:18:51.157048Z","submitted_at":"2026-05-07T14:56:56Z","title":"Independent Learning of Nash Equilibria in Partially Observable Markov Potential Games with Decoupled Dynamics","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-05-08T04:21:21.394989Z"},"links":{"citing_paper":"/paper/2605.06377"},"observation_digest":"sha256:29e63ba69941e21a5acc4ee65635b46bd639619faf66c89a1fea73ba24ce6028","observation_id":"60dc3f7c-de65-402b-88d7-9b7b0b5f5f59","resolution":{"observed_at":"2026-05-26T21:08:03.349329Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Solving imperfect information Poker games using Monte Carlo search and POMDP models","venue":null,"work_id":"111e2f84-fbc2-47be-bcf0-75bb63199419","year":2020},"citing_paper":{"arxiv_id":"2605.06377","last_updated":"2026-05-07T14:56:56Z","snapshot_observed_at":"2026-07-06T23:18:51.157048Z","submitted_at":"2026-05-07T14:56:56Z","title":"Independent Learning of Nash Equilibria in Partially Observable Markov Potential Games with Decoupled Dynamics","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-05-08T04:21:21.394989Z"},"links":{"citing_paper":"/paper/2605.06377"},"observation_digest":"sha256:bb86778a0f95d223214d099b1843d6af3349bfaa5e8e2b5ed6941f70a345e743","observation_id":"e91fb2d8-b0bd-4870-940c-154ed73709fc","resolution":{"observed_at":"2026-05-26T21:08:03.359460Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Markov decision processes: Discrete stochastic dynamic programming","venue":null,"work_id":"7f960e6b-5957-487e-bf65-cf8e255c404d","year":1994},"citing_paper":{"arxiv_id":"2605.06377","last_updated":"2026-05-07T14:56:56Z","snapshot_observed_at":"2026-07-06T23:18:51.157048Z","submitted_at":"2026-05-07T14:56:56Z","title":"Independent Learning of Nash Equilibria in Partially Observable Markov Potential Games with Decoupled Dynamics","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-05-08T04:21:21.394989Z"},"links":{"citing_paper":"/paper/2605.06377"},"observation_digest":"sha256:ecd96c6a5ea6b60df0c250e58b30ac5470c65875e6bf60d38e55447094e5050c","observation_id":"718c7368-0bf2-4899-95ea-d4b2004c72a4","resolution":{"observed_at":"2026-05-26T21:08:03.200973Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"The complexity of Markov decision processes","venue":null,"work_id":"1d75de87-cc92-4cf3-b398-4e17649785e9","year":1987},"citing_paper":{"arxiv_id":"2605.06377","last_updated":"2026-05-07T14:56:56Z","snapshot_observed_at":"2026-07-06T23:18:51.157048Z","submitted_at":"2026-05-07T14:56:56Z","title":"Independent Learning of Nash Equilibria in Partially Observable Markov Potential Games with Decoupled Dynamics","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-05-08T04:21:21.394989Z"},"links":{"citing_paper":"/paper/2605.06377"},"observation_digest":"sha256:6d3621a3d04f24e9b16d209c68147ada3b4ad8fcd55053ac2dcc9e8d51377e41","observation_id":"216901d5-1719-4ad0-91a6-474726f676c2","resolution":{"observed_at":"2026-05-26T21:08:03.328980Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"PAC reinforcement learning with rich observations.Advances in Neural Information Processing Systems","venue":null,"work_id":"305bc5cf-45f2-4fb5-82c5-333bc35a6fde","year":2016},"citing_paper":{"arxiv_id":"2605.06377","last_updated":"2026-05-07T14:56:56Z","snapshot_observed_at":"2026-07-06T23:18:51.157048Z","submitted_at":"2026-05-07T14:56:56Z","title":"Independent Learning of Nash Equilibria in Partially Observable Markov Potential Games with Decoupled Dynamics","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-05-08T04:21:21.394989Z"},"links":{"citing_paper":"/paper/2605.06377"},"observation_digest":"sha256:4b8eee15d138172991cc8c63b590fef90be8e1f25283a0224500ef392b69eb5f","observation_id":"19e2eb72-d70b-4238-9c14-69738723d874","resolution":{"observed_at":"2026-05-26T21:08:03.308286Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Planning and learning in partially observ- able systems via filter stability","venue":null,"work_id":"8680a322-35bc-4fe9-89f1-deabd5cd0bf9","year":2023},"citing_paper":{"arxiv_id":"2605.06377","last_updated":"2026-05-07T14:56:56Z","snapshot_observed_at":"2026-07-06T23:18:51.157048Z","submitted_at":"2026-05-07T14:56:56Z","title":"Independent Learning of Nash Equilibria in Partially Observable Markov Potential Games with Decoupled Dynamics","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-05-08T04:21:21.394989Z"},"links":{"citing_paper":"/paper/2605.06377"},"observation_digest":"sha256:92bb509fe3fa81614eb9ff12a445a79912a31c178ba7e4ee08db3f94acf3e525","observation_id":"6587cb37-08d3-4b53-be0f-bbb3ba89c05e","resolution":{"observed_at":"2026-05-26T21:08:03.274462Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"When is partially observable reinforcement learning not scary? InConference on Learning Theory, pages 5175–5220","venue":null,"work_id":"896a3359-83d4-4503-920b-955bb89b05bd","year":2022},"citing_paper":{"arxiv_id":"2605.06377","last_updated":"2026-05-07T14:56:56Z","snapshot_observed_at":"2026-07-06T23:18:51.157048Z","submitted_at":"2026-05-07T14:56:56Z","title":"Independent Learning of Nash Equilibria in Partially Observable Markov Potential Games with Decoupled Dynamics","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-05-08T04:21:21.394989Z"},"links":{"citing_paper":"/paper/2605.06377"},"observation_digest":"sha256:cd4d776729e9917dbc35e4dc9987c33b7ea4d55d9b3ed4d0b8c0c6658883ef8a","observation_id":"db418b22-91ae-40ab-b033-e87ee77dd27e","resolution":{"observed_at":"2026-05-26T21:08:03.249594Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Learning in observable POMDPs, without computationally intractable oracles.Advances in Neural Information Processing Systems","venue":null,"work_id":"591a54eb-a409-43b8-87a4-411d5a6e27f6","year":2022},"citing_paper":{"arxiv_id":"2605.06377","last_updated":"2026-05-07T14:56:56Z","snapshot_observed_at":"2026-07-06T23:18:51.157048Z","submitted_at":"2026-05-07T14:56:56Z","title":"Independent Learning of Nash Equilibria in Partially Observable Markov Potential Games with Decoupled Dynamics","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-05-08T04:21:21.394989Z"},"links":{"citing_paper":"/paper/2605.06377"},"observation_digest":"sha256:b48d445e99631c2ba33073796a1a1b4ca26a23aa14e709f44182136efc5c9fef","observation_id":"97f5c070-e5fd-4db9-8d85-9296d556f8bb","resolution":{"observed_at":"2026-05-26T21:08:03.252575Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"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","venue":null,"work_id":"bca72177-e5ab-469b-b0eb-ba29d86d0a1f","year":2066},"citing_paper":{"arxiv_id":"2605.06377","last_updated":"2026-05-07T14:56:56Z","snapshot_observed_at":"2026-07-06T23:18:51.157048Z","submitted_at":"2026-05-07T14:56:56Z","title":"Independent Learning of Nash Equilibria in Partially Observable Markov Potential Games with Decoupled Dynamics","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-05-08T04:21:21.394989Z"},"links":{"citing_paper":"/paper/2605.06377"},"observation_digest":"sha256:1427c965a394891bde26fba35cf4ee13f3289bce9eb21cca2d1de554b9a3436a","observation_id":"e5c5ed3f-a62b-4437-b0f7-ee9ca53c3633","resolution":{"observed_at":"2026-05-26T21:08:03.271183Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Finite-time analysis of natural actor-critic for POMDPs","venue":null,"work_id":"6a45d5ac-b4b9-4fed-b844-2406f878e38f","year":2024},"citing_paper":{"arxiv_id":"2605.06377","last_updated":"2026-05-07T14:56:56Z","snapshot_observed_at":"2026-07-06T23:18:51.157048Z","submitted_at":"2026-05-07T14:56:56Z","title":"Independent Learning of Nash Equilibria in Partially Observable Markov Potential Games with Decoupled Dynamics","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-05-08T04:21:21.394989Z"},"links":{"citing_paper":"/paper/2605.06377"},"observation_digest":"sha256:00f50ecd384cc1877e5dbc6f3f31df5f3634227589084d3e506eb8847f0b6be8","observation_id":"630d03f8-96af-4ee4-9748-7a8efa884a78","resolution":{"observed_at":"2026-05-26T21:08:03.230804Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Scalable policy-based RL algorithms for POMDPs.Advances in Neural Information Processing Systems","venue":null,"work_id":"28d8ca48-2b0e-407b-a945-08f49a8d3e9c","year":2025},"citing_paper":{"arxiv_id":"2605.06377","last_updated":"2026-05-07T14:56:56Z","snapshot_observed_at":"2026-07-06T23:18:51.157048Z","submitted_at":"2026-05-07T14:56:56Z","title":"Independent Learning of Nash Equilibria in Partially Observable Markov Potential Games with Decoupled Dynamics","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-05-08T04:21:21.394989Z"},"links":{"citing_paper":"/paper/2605.06377"},"observation_digest":"sha256:88a1ea329643a0ced5c240fe140ee19900e021d86d4873b083284763700b45f6","observation_id":"79725dd4-9768-41b9-937b-c2eacbf48c3f","resolution":{"observed_at":"2026-05-26T21:08:03.234750Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2604.01024","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Model-based learning of near-optimal finite-window policies in POMDPs","venue":null,"work_id":"c59fc1b8-19a4-4458-b991-27c5de83397e","year":2026},"citing_paper":{"arxiv_id":"2605.06377","last_updated":"2026-05-07T14:56:56Z","snapshot_observed_at":"2026-07-06T23:18:51.157048Z","submitted_at":"2026-05-07T14:56:56Z","title":"Independent Learning of Nash Equilibria in Partially Observable Markov Potential Games with Decoupled Dynamics","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-05-08T04:21:21.394989Z"},"links":{"citing_paper":"/paper/2605.06377"},"observation_digest":"sha256:c1f434a8b603ef8a7776ed0a6c4a2f2d62dd08faeb8611772c904dcb5e24a9a9","observation_id":"61de9c0a-7437-42da-9523-1afae936d18a","resolution":{"observed_at":"2026-05-11T21:46:43.629055Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Approxi- mate solutions for partially observable stochastic games with common payoffs","venue":null,"work_id":"d3d7b547-2448-4e51-b8c6-1800783823a8","year":null},"citing_paper":{"arxiv_id":"2605.06377","last_updated":"2026-05-07T14:56:56Z","snapshot_observed_at":"2026-07-06T23:18:51.157048Z","submitted_at":"2026-05-07T14:56:56Z","title":"Independent Learning of Nash Equilibria in Partially Observable Markov Potential Games with Decoupled Dynamics","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-05-08T04:21:21.394989Z"},"links":{"citing_paper":"/paper/2605.06377"},"observation_digest":"sha256:e12a4f31e6c0fd4cdf547777e9346a2e7a3589214565023ee9b2987cd198334c","observation_id":"2d54477b-ed41-4314-b4b7-9b2741d369c2","resolution":{"observed_at":"2026-05-26T21:08:03.238445Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"IEEE","venue":null,"work_id":"67b87dbe-0460-41d0-b1ef-68221fa24766","year":2004},"citing_paper":{"arxiv_id":"2605.06377","last_updated":"2026-05-07T14:56:56Z","snapshot_observed_at":"2026-07-06T23:18:51.157048Z","submitted_at":"2026-05-07T14:56:56Z","title":"Independent Learning of Nash Equilibria in Partially Observable Markov Potential Games with Decoupled Dynamics","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-05-08T04:21:21.394989Z"},"links":{"citing_paper":"/paper/2605.06377"},"observation_digest":"sha256:601d5736a43af2c1f676acd9516eccc00377899bcd2149ab7b1768375cbb3c74","observation_id":"6c511b5e-da33-4da1-9ae0-51d37c631972","resolution":{"observed_at":"2026-05-26T21:08:03.241904Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Deep decentralized multi-task multi-agent reinforcement learning under partial observability","venue":null,"work_id":"2b6dcb60-a079-42da-9217-e915bc28ff7c","year":2017},"citing_paper":{"arxiv_id":"2605.06377","last_updated":"2026-05-07T14:56:56Z","snapshot_observed_at":"2026-07-06T23:18:51.157048Z","submitted_at":"2026-05-07T14:56:56Z","title":"Independent Learning of Nash Equilibria in Partially Observable Markov Potential Games with Decoupled Dynamics","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-05-08T04:21:21.394989Z"},"links":{"citing_paper":"/paper/2605.06377"},"observation_digest":"sha256:d44b83561e26f6928aa3f166340f260abe10f7937bf4b72559383e80ea101da8","observation_id":"9e1ac18e-da63-4668-b46d-ba88e23012e6","resolution":{"observed_at":"2026-05-26T21:08:03.280618Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Improving policies via search in cooperative partially observable games","venue":null,"work_id":"6211bdff-8663-466e-b977-69867e258b84","year":2020},"citing_paper":{"arxiv_id":"2605.06377","last_updated":"2026-05-07T14:56:56Z","snapshot_observed_at":"2026-07-06T23:18:51.157048Z","submitted_at":"2026-05-07T14:56:56Z","title":"Independent Learning of Nash Equilibria in Partially Observable Markov Potential Games with Decoupled Dynamics","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-05-08T04:21:21.394989Z"},"links":{"citing_paper":"/paper/2605.06377"},"observation_digest":"sha256:655c826a11a01b220a96da2d2a4e93b930c612cf2113d76ca50d0849282fd8b5","observation_id":"3f86a19f-8d39-42f1-b2c9-e337843aa0ae","resolution":{"observed_at":"2026-05-26T21:08:03.293501Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Divergence-regularized discounted aggregation: Equilibrium finding in multiplayer partially observable stochastic games","venue":null,"work_id":"974c6099-ec7a-4ac2-9120-45b44cc57fb0","year":2025},"citing_paper":{"arxiv_id":"2605.06377","last_updated":"2026-05-07T14:56:56Z","snapshot_observed_at":"2026-07-06T23:18:51.157048Z","submitted_at":"2026-05-07T14:56:56Z","title":"Independent Learning of Nash Equilibria in Partially Observable Markov Potential Games with Decoupled Dynamics","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-05-08T04:21:21.394989Z"},"links":{"citing_paper":"/paper/2605.06377"},"observation_digest":"sha256:4d7e0aacb831d36ff5bee6cda053ab741263d5bdb1bd7287030974cf3a93b780","observation_id":"d6907efa-6d22-4145-baf3-51cef0396577","resolution":{"observed_at":"2026-05-26T21:08:03.217431Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Sample-efficient reinforcement learning of partially observable Markov games.Advances in Neural Information Processing Systems","venue":null,"work_id":"df28f1af-1ba1-4338-af0e-3ebe18121d5e","year":2022},"citing_paper":{"arxiv_id":"2605.06377","last_updated":"2026-05-07T14:56:56Z","snapshot_observed_at":"2026-07-06T23:18:51.157048Z","submitted_at":"2026-05-07T14:56:56Z","title":"Independent Learning of Nash Equilibria in Partially Observable Markov Potential Games with Decoupled Dynamics","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-05-08T04:21:21.394989Z"},"links":{"citing_paper":"/paper/2605.06377"},"observation_digest":"sha256:6db65b8b396dee930c518fbb344c87adfaec0f383dda277d6a6ff2b3ebf91617","observation_id":"7fee00d0-be4c-4023-9da1-fa22cda71002","resolution":{"observed_at":"2026-05-26T21:08:03.223240Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"The complexity of computing a Nash equilibrium.Communications of the ACM, 52(2):89–97","venue":null,"work_id":"c6219d8f-1799-40aa-b21c-4e779d400bd2","year":2009},"citing_paper":{"arxiv_id":"2605.06377","last_updated":"2026-05-07T14:56:56Z","snapshot_observed_at":"2026-07-06T23:18:51.157048Z","submitted_at":"2026-05-07T14:56:56Z","title":"Independent Learning of Nash Equilibria in Partially Observable Markov Potential Games with Decoupled Dynamics","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-05-08T04:21:21.394989Z"},"links":{"citing_paper":"/paper/2605.06377"},"observation_digest":"sha256:bbff22369aa5fa44511755b91b4a63c97b69f86457db74c916875ae08417aec1","observation_id":"e2d44f1b-8f73-4a26-82ff-fe0f7f13826d","resolution":{"observed_at":"2026-05-26T21:08:03.204272Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Learning parametric closed-loop policies for Markov potential games","venue":null,"work_id":"64b27d4e-7b12-467c-a10c-ae5f460c6f08","year":2018},"citing_paper":{"arxiv_id":"2605.06377","last_updated":"2026-05-07T14:56:56Z","snapshot_observed_at":"2026-07-06T23:18:51.157048Z","submitted_at":"2026-05-07T14:56:56Z","title":"Independent Learning of Nash Equilibria in Partially Observable Markov Potential Games with Decoupled Dynamics","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-05-08T04:21:21.394989Z"},"links":{"citing_paper":"/paper/2605.06377"},"observation_digest":"sha256:518f05a86ac929974a178486727e62386a11bae2603bbcccec955e995e283018","observation_id":"49985356-1745-4260-a416-d02755310584","resolution":{"observed_at":"2026-05-26T21:08:03.207487Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"When can we learn general-sum Markov games with a large number of players sample-efficiently? InInternational Conference on Learning Representations","venue":null,"work_id":"1fa406c5-dda4-4077-a14b-c4c58988486e","year":2022},"citing_paper":{"arxiv_id":"2605.06377","last_updated":"2026-05-07T14:56:56Z","snapshot_observed_at":"2026-07-06T23:18:51.157048Z","submitted_at":"2026-05-07T14:56:56Z","title":"Independent Learning of Nash Equilibria in Partially Observable Markov Potential Games with Decoupled Dynamics","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-05-08T04:21:21.394989Z"},"links":{"citing_paper":"/paper/2605.06377"},"observation_digest":"sha256:1333444f07e63b3f88fba8b05d0d331a8c314ef60b326dff5d2a900705265bc8","observation_id":"a922cc57-9e25-4787-a76d-09ab7f945ad9","resolution":{"observed_at":"2026-05-26T21:08:03.196282Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Global conver- gence of multi-agent policy gradient in Markov potential games","venue":null,"work_id":"dbfae84c-a148-49be-ad98-6c01f3a99a7c","year":2022},"citing_paper":{"arxiv_id":"2605.06377","last_updated":"2026-05-07T14:56:56Z","snapshot_observed_at":"2026-07-06T23:18:51.157048Z","submitted_at":"2026-05-07T14:56:56Z","title":"Independent Learning of Nash Equilibria in Partially Observable Markov Potential Games with Decoupled Dynamics","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-05-08T04:21:21.394989Z"},"links":{"citing_paper":"/paper/2605.06377"},"observation_digest":"sha256:1f68f5b6038ed57c1e0e455b12265283e47dbb66ca75e04008c2c21fd5abd665","observation_id":"a1130373-812e-447b-84fe-949512bfba3e","resolution":{"observed_at":"2026-05-26T21:08:03.214346Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Independent policy gradient for large-scale Markov potential games: Sharper rates, function approximation, and game-agnostic convergence","venue":null,"work_id":"83ebe5b6-4360-45f0-9333-3eb86b44ba64","year":null},"citing_paper":{"arxiv_id":"2605.06377","last_updated":"2026-05-07T14:56:56Z","snapshot_observed_at":"2026-07-06T23:18:51.157048Z","submitted_at":"2026-05-07T14:56:56Z","title":"Independent Learning of Nash Equilibria in Partially Observable Markov Potential Games with Decoupled Dynamics","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-05-08T04:21:21.394989Z"},"links":{"citing_paper":"/paper/2605.06377"},"observation_digest":"sha256:683631eabaea5127286686f14add6a436f70aabef009873972d5fdf8edd9510d","observation_id":"4a91d1b2-4bc6-4422-8dab-2f17d885407a","resolution":{"observed_at":"2026-05-26T21:08:03.220442Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Independent and decentralized learning in Markov potential games.IEEE Transactions on Automatic Control","venue":null,"work_id":"8cab20dc-182c-4cec-a82e-fd2c1dc19fe9","year":2025},"citing_paper":{"arxiv_id":"2605.06377","last_updated":"2026-05-07T14:56:56Z","snapshot_observed_at":"2026-07-06T23:18:51.157048Z","submitted_at":"2026-05-07T14:56:56Z","title":"Independent Learning of Nash Equilibria in Partially Observable Markov Potential Games with Decoupled Dynamics","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-05-08T04:21:21.394989Z"},"links":{"citing_paper":"/paper/2605.06377"},"observation_digest":"sha256:44084854628eb194d100857cae92d256a659d04f67350f19dde0ee4a63a5b14d","observation_id":"56ffb599-33aa-47b1-8122-7fc53dd71917","resolution":{"observed_at":"2026-05-26T21:08:03.346062Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"The complexity of decentralized control of Markov decision processes.Mathematics of operations research, 27(4): 819–840","venue":null,"work_id":"0b93bc5a-0506-4141-b521-b990d0333056","year":2002},"citing_paper":{"arxiv_id":"2605.06377","last_updated":"2026-05-07T14:56:56Z","snapshot_observed_at":"2026-07-06T23:18:51.157048Z","submitted_at":"2026-05-07T14:56:56Z","title":"Independent Learning of Nash Equilibria in Partially Observable Markov Potential Games with Decoupled Dynamics","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-05-08T04:21:21.394989Z"},"links":{"citing_paper":"/paper/2605.06377"},"observation_digest":"sha256:9ae3dd30a901c77fb4189741bab180a01538e3814511f4cff15b18d4d72413aa","observation_id":"6bd9cd5b-f191-4ade-88e9-b48a0e1d43ff","resolution":{"observed_at":"2026-05-26T21:08:03.210456Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Optimally solving Dec-POMDPs as continuous-state MDPs.Journal of Artificial Intelligence Research, 55:443–497","venue":null,"work_id":"24dae75a-8787-4881-b04b-9798b1ad443a","year":2016},"citing_paper":{"arxiv_id":"2605.06377","last_updated":"2026-05-07T14:56:56Z","snapshot_observed_at":"2026-07-06T23:18:51.157048Z","submitted_at":"2026-05-07T14:56:56Z","title":"Independent Learning of Nash Equilibria in Partially Observable Markov Potential Games with Decoupled Dynamics","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-05-08T04:21:21.394989Z"},"links":{"citing_paper":"/paper/2605.06377"},"observation_digest":"sha256:fc2b66a29828371dddd41c1d94a5d1a8c970c8e1fd457440065f3b8e194c729f","observation_id":"3ab58e87-d03d-44a5-94a4-c02db799cb7c","resolution":{"observed_at":"2026-05-26T21:08:03.226614Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Learning to act in decentralized partially observable MDPs","venue":null,"work_id":"c437e2b1-7a6c-495e-af20-af04cb3492c4","year":2018},"citing_paper":{"arxiv_id":"2605.06377","last_updated":"2026-05-07T14:56:56Z","snapshot_observed_at":"2026-07-06T23:18:51.157048Z","submitted_at":"2026-05-07T14:56:56Z","title":"Independent Learning of Nash Equilibria in Partially Observable Markov Potential Games with Decoupled Dynamics","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-05-08T04:21:21.394989Z"},"links":{"citing_paper":"/paper/2605.06377"},"observation_digest":"sha256:1d85b7143404b61011a09a3539615492fae8918d2a6db6eb50cd18301c2c4f12","observation_id":"33181333-6837-463f-8318-8ce7afc662fb","resolution":{"observed_at":"2026-05-26T21:08:03.290221Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Decentralized learning of finite-memory policies in Dec-POMDPs.IFAC-PapersOnLine, 56(2):2601–2607","venue":null,"work_id":"05e64d2e-2b51-4c0e-a180-42322d0cb370","year":2023},"citing_paper":{"arxiv_id":"2605.06377","last_updated":"2026-05-07T14:56:56Z","snapshot_observed_at":"2026-07-06T23:18:51.157048Z","submitted_at":"2026-05-07T14:56:56Z","title":"Independent Learning of Nash Equilibria in Partially Observable Markov Potential Games with Decoupled Dynamics","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-05-08T04:21:21.394989Z"},"links":{"citing_paper":"/paper/2605.06377"},"observation_digest":"sha256:103ac901f6fcd6c2aa7b7696e568b7e3c2ad2164939b598210bda5244ce719c1","observation_id":"da020291-180d-4c92-b5be-a41e0be5fc47","resolution":{"observed_at":"2026-05-26T21:08:03.342616Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Partially observable multi-agent rl with (quasi-) efficiency: The blessing of information sharing","venue":null,"work_id":"f26fb19e-054c-4eb5-93ae-f5b90982aacd","year":2023},"citing_paper":{"arxiv_id":"2605.06377","last_updated":"2026-05-07T14:56:56Z","snapshot_observed_at":"2026-07-06T23:18:51.157048Z","submitted_at":"2026-05-07T14:56:56Z","title":"Independent Learning of Nash Equilibria in Partially Observable Markov Potential Games with Decoupled Dynamics","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-05-08T04:21:21.394989Z"},"links":{"citing_paper":"/paper/2605.06377"},"observation_digest":"sha256:0ced19413c027e230ef166a11f784a46e90c2c92782e47ea6f66d17d72dc7be3","observation_id":"8defbd85-8b5a-4554-a9ef-02bcfdafaeb4","resolution":{"observed_at":"2026-05-26T21:08:03.335667Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Constrained stochastic games in wireless networks","venue":null,"work_id":"45646fc6-24e6-412d-8ed9-a6cf82d0e850","year":2007},"citing_paper":{"arxiv_id":"2605.06377","last_updated":"2026-05-07T14:56:56Z","snapshot_observed_at":"2026-07-06T23:18:51.157048Z","submitted_at":"2026-05-07T14:56:56Z","title":"Independent Learning of Nash Equilibria in Partially Observable Markov Potential Games with Decoupled Dynamics","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-05-08T04:21:21.394989Z"},"links":{"citing_paper":"/paper/2605.06377"},"observation_digest":"sha256:9ff9d031be751ca811b209904e928a2532b2338b11a4c04a921daa259d0e3c6a","observation_id":"de4ce7a4-662f-4c0e-94d4-f5b036a44900","resolution":{"observed_at":"2026-05-26T21:08:03.338984Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Dynamic discrete power control in cellular networks.IEEE Transactions on Automatic Control, 54(10):2328–2340","venue":null,"work_id":"c72fae0c-ad6b-4d40-aa78-821e10015bb2","year":2009},"citing_paper":{"arxiv_id":"2605.06377","last_updated":"2026-05-07T14:56:56Z","snapshot_observed_at":"2026-07-06T23:18:51.157048Z","submitted_at":"2026-05-07T14:56:56Z","title":"Independent Learning of Nash Equilibria in Partially Observable Markov Potential Games with Decoupled Dynamics","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-05-08T04:21:21.394989Z"},"links":{"citing_paper":"/paper/2605.06377"},"observation_digest":"sha256:84566c15d271b44806f4ddc2a6a465fa3e34b7e83c579ce006c44f694b94869e","observation_id":"345683cb-5f3e-45aa-809f-b6f6f079532d","resolution":{"observed_at":"2026-05-26T21:08:03.318304Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Stochastic games for the smart grid energy management with prospect prosumers.IEEE Transactions on Automatic Control, 63(8):2327–2342","venue":null,"work_id":"3f83106a-a507-40e8-9bf9-59b8c6c10383","year":2018},"citing_paper":{"arxiv_id":"2605.06377","last_updated":"2026-05-07T14:56:56Z","snapshot_observed_at":"2026-07-06T23:18:51.157048Z","submitted_at":"2026-05-07T14:56:56Z","title":"Independent Learning of Nash Equilibria in Partially Observable Markov Potential Games with Decoupled Dynamics","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-05-08T04:21:21.394989Z"},"links":{"citing_paper":"/paper/2605.06377"},"observation_digest":"sha256:cac2d6ab91debfb7a6f97da83db8473258bdd7c22fbc1146745484c15ec3f5bc","observation_id":"78d9832a-357d-4e45-ae6c-9c69f690b7f8","resolution":{"observed_at":"2026-05-26T21:08:03.322138Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Markov games with decoupled dynamics: Price of anarchy and sample complexity","venue":null,"work_id":"c608f842-0ee8-4758-aea1-d0df89b8bbcf","year":2023},"citing_paper":{"arxiv_id":"2605.06377","last_updated":"2026-05-07T14:56:56Z","snapshot_observed_at":"2026-07-06T23:18:51.157048Z","submitted_at":"2026-05-07T14:56:56Z","title":"Independent Learning of Nash Equilibria in Partially Observable Markov Potential Games with Decoupled Dynamics","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-05-08T04:21:21.394989Z"},"links":{"citing_paper":"/paper/2605.06377"},"observation_digest":"sha256:ddd1bd644be6e8a01b292f285d9648d3ad18c13266cc6b202f8e10ee733cc981","observation_id":"9fe805af-d92f-47cb-af69-a16a975aa1dd","resolution":{"observed_at":"2026-05-26T21:08:03.311731Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Learning stationary nash equilibrium policies in n-player stochastic games with independent chains.SIAM Journal on Control and Optimization, 62(2):799–825","venue":null,"work_id":"32421356-b11f-458e-8e11-6ef4415c95ca","year":2024},"citing_paper":{"arxiv_id":"2605.06377","last_updated":"2026-05-07T14:56:56Z","snapshot_observed_at":"2026-07-06T23:18:51.157048Z","submitted_at":"2026-05-07T14:56:56Z","title":"Independent Learning of Nash Equilibria in Partially Observable Markov Potential Games with Decoupled Dynamics","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-05-08T04:21:21.394989Z"},"links":{"citing_paper":"/paper/2605.06377"},"observation_digest":"sha256:7e84f1f002ac9bf3490a149a4904db21c494f3d797426a0ea5ec889a7d8fc74f","observation_id":"b67fce2c-8b0f-47dc-acda-f7eeb144b1c8","resolution":{"observed_at":"2026-05-26T21:08:03.314917Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Markov α-potential games.IEEE Transactions on Automatic Control","venue":null,"work_id":"d6ec64b0-2de2-49da-9fa5-563589eeed07","year":2025},"citing_paper":{"arxiv_id":"2605.06377","last_updated":"2026-05-07T14:56:56Z","snapshot_observed_at":"2026-07-06T23:18:51.157048Z","submitted_at":"2026-05-07T14:56:56Z","title":"Independent Learning of Nash Equilibria in Partially Observable Markov Potential Games with Decoupled Dynamics","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-05-08T04:21:21.394989Z"},"links":{"citing_paper":"/paper/2605.06377"},"observation_digest":"sha256:0aaae27da880df21ec641d3c64f63651794e61a75e5ab40c0906ddfa8a18c1ad","observation_id":"e8c3a4c7-7919-48f6-9b2f-ad466bff21f1","resolution":{"observed_at":"2026-05-26T21:08:03.300107Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"An α-potential game framework for n-player dynamic games.SIAM Journal on Control and Optimization, 63(4):2964–3005","venue":null,"work_id":"5bfef24a-3fa4-4988-a8e8-e4fd49ced7b6","year":2025},"citing_paper":{"arxiv_id":"2605.06377","last_updated":"2026-05-07T14:56:56Z","snapshot_observed_at":"2026-07-06T23:18:51.157048Z","submitted_at":"2026-05-07T14:56:56Z","title":"Independent Learning of Nash Equilibria in Partially Observable Markov Potential Games with Decoupled Dynamics","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-05-08T04:21:21.394989Z"},"links":{"citing_paper":"/paper/2605.06377"},"observation_digest":"sha256:517accc03dc44aa01ff79e639262e02a9934c32647676fb67aaf39bd9789368d","observation_id":"bbb1df95-7d41-433c-8d84-892c3e1566c2","resolution":{"observed_at":"2026-05-26T21:08:03.303621Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"On the global convergence rates of decentralized softmax gradient play in Markov potential games.Advances in Neural Information Processing Systems","venue":null,"work_id":"933c879c-4e2a-4052-8e0d-d3b60e2e883a","year":2022},"citing_paper":{"arxiv_id":"2605.06377","last_updated":"2026-05-07T14:56:56Z","snapshot_observed_at":"2026-07-06T23:18:51.157048Z","submitted_at":"2026-05-07T14:56:56Z","title":"Independent Learning of Nash Equilibria in Partially Observable Markov Potential Games with Decoupled Dynamics","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-05-08T04:21:21.394989Z"},"links":{"citing_paper":"/paper/2605.06377"},"observation_digest":"sha256:1dbabb2225541e80fa8c8f4e7852d287f6f1f04b3dba5bc99ddcace51ad50d0c","observation_id":"aa54e1d0-6210-491f-adf6-b84677923844","resolution":{"observed_at":"2026-05-26T21:08:03.325810Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Multi-agent learning via Markov potential games in marketplaces for distributed energy resources","venue":null,"work_id":"dc155990-4cc5-4c46-a527-e924d0fdbea2","year":2022},"citing_paper":{"arxiv_id":"2605.06377","last_updated":"2026-05-07T14:56:56Z","snapshot_observed_at":"2026-07-06T23:18:51.157048Z","submitted_at":"2026-05-07T14:56:56Z","title":"Independent Learning of Nash Equilibria in Partially Observable Markov Potential Games with Decoupled Dynamics","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-05-08T04:21:21.394989Z"},"links":{"citing_paper":"/paper/2605.06377"},"observation_digest":"sha256:2b1d20c7a1eb053a58710de55d1bd60ab1bf871ad89d76e7eb65a91e643f19d6","observation_id":"dae29f47-cec7-42ad-a76c-443f99e7428b","resolution":{"observed_at":"2026-05-26T21:08:03.332452Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Hidden Markov models.Unpublished lecture notes","venue":null,"work_id":"fbfc95aa-2f77-4f36-8b3c-3e52e5bcfce6","year":2008},"citing_paper":{"arxiv_id":"2605.06377","last_updated":"2026-05-07T14:56:56Z","snapshot_observed_at":"2026-07-06T23:18:51.157048Z","submitted_at":"2026-05-07T14:56:56Z","title":"Independent Learning of Nash Equilibria in Partially Observable Markov Potential Games with Decoupled Dynamics","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-05-08T04:21:21.394989Z"},"links":{"citing_paper":"/paper/2605.06377"},"observation_digest":"sha256:a6e268feb35188fc492d3a34ef304d40b9ec5f6dbe284112277e9ef4e687f55c","observation_id":"6a1e3599-0046-4943-bb08-e1625495e414","resolution":{"observed_at":"2026-05-26T21:08:03.283642Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Near optimality of finite memory feedback policies in partially observed Markov decision processes.Journal of Machine Learning Research, 23(11):1–46","venue":null,"work_id":"910a96c7-27ed-47a7-afa4-f5ba30d05bd3","year":2022},"citing_paper":{"arxiv_id":"2605.06377","last_updated":"2026-05-07T14:56:56Z","snapshot_observed_at":"2026-07-06T23:18:51.157048Z","submitted_at":"2026-05-07T14:56:56Z","title":"Independent Learning of Nash Equilibria in Partially Observable Markov Potential Games with Decoupled Dynamics","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-05-08T04:21:21.394989Z"},"links":{"citing_paper":"/paper/2605.06377"},"observation_digest":"sha256:a69577d11a4747f40ea9ef8c5c0b0b75e2c74af8110300091fbf4fedb2d86a6b","observation_id":"593d4d7e-5fdc-4dbc-ad35-1f5697fddc83","resolution":{"observed_at":"2026-05-26T21:08:03.296997Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Multi-agent reinforcement learning: A selective overview of theories and algorithms.Handbook of reinforcement learning and control, pages 321–384","venue":null,"work_id":"bd104330-c399-4b16-856d-260b885a4015","year":2021},"citing_paper":{"arxiv_id":"2605.06377","last_updated":"2026-05-07T14:56:56Z","snapshot_observed_at":"2026-07-06T23:18:51.157048Z","submitted_at":"2026-05-07T14:56:56Z","title":"Independent Learning of Nash Equilibria in Partially Observable Markov Potential Games with Decoupled Dynamics","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-05-08T04:21:21.394989Z"},"links":{"citing_paper":"/paper/2605.06377"},"observation_digest":"sha256:dfff6e02af8b5f1184b399e5ae51d4f18c812d9a32e5ffd9cbdd5980a0e346c9","observation_id":"ad83b200-55d3-4e67-8760-02942512fe24","resolution":{"observed_at":"2026-05-26T21:08:03.264407Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Independent policy gradient methods for competitive reinforcement learning.Advances in Neural Information Processing Systems","venue":null,"work_id":"a280f2d0-8549-4ce3-839a-c52446c82227","year":2020},"citing_paper":{"arxiv_id":"2605.06377","last_updated":"2026-05-07T14:56:56Z","snapshot_observed_at":"2026-07-06T23:18:51.157048Z","submitted_at":"2026-05-07T14:56:56Z","title":"Independent Learning of Nash Equilibria in Partially Observable Markov Potential Games with Decoupled Dynamics","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-05-08T04:21:21.394989Z"},"links":{"citing_paper":"/paper/2605.06377"},"observation_digest":"sha256:f934cc9dfc6db9f281f0d79e2cc6bfd9b2928327022c8d27c3ec107b42b61b45","observation_id":"091b3b8c-6cb5-4010-91ef-cb930369dba9","resolution":{"observed_at":"2026-05-26T21:08:03.267895Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Provable self-play algorithms for competitive reinforcement learning","venue":null,"work_id":"8fb9c67c-89ae-4171-aa0b-206f9402ea30","year":2020},"citing_paper":{"arxiv_id":"2605.06377","last_updated":"2026-05-07T14:56:56Z","snapshot_observed_at":"2026-07-06T23:18:51.157048Z","submitted_at":"2026-05-07T14:56:56Z","title":"Independent Learning of Nash Equilibria in Partially Observable Markov Potential Games with Decoupled Dynamics","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-05-08T04:21:21.394989Z"},"links":{"citing_paper":"/paper/2605.06377"},"observation_digest":"sha256:36c7a7d5d0fe3b43163f558e7e66555565eaa5c8eb8437b34082b9da641b2b0b","observation_id":"98e7f92a-bcaa-4822-a682-e53caa3dc0cd","resolution":{"observed_at":"2026-05-26T21:08:03.277401Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Cyclic equilibria in Markov games","venue":null,"work_id":"feedb96c-a45e-45b0-9875-170814395e68","year":2005},"citing_paper":{"arxiv_id":"2605.06377","last_updated":"2026-05-07T14:56:56Z","snapshot_observed_at":"2026-07-06T23:18:51.157048Z","submitted_at":"2026-05-07T14:56:56Z","title":"Independent Learning of Nash Equilibria in Partially Observable Markov Potential Games with Decoupled Dynamics","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-05-08T04:21:21.394989Z"},"links":{"citing_paper":"/paper/2605.06377"},"observation_digest":"sha256:4c69868d94be2b64dd544a524d301012bf1fbfaed3b39d2bb6aad734df40a157","observation_id":"9e2c2821-9532-44f6-bebb-577fdc4ef108","resolution":{"observed_at":"2026-05-26T21:08:03.255859Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"On the sample complexity of reinforcement learning with a generative model","venue":null,"work_id":"59af12eb-1762-48ba-8266-7496c4627a03","year":2012},"citing_paper":{"arxiv_id":"2605.06377","last_updated":"2026-05-07T14:56:56Z","snapshot_observed_at":"2026-07-06T23:18:51.157048Z","submitted_at":"2026-05-07T14:56:56Z","title":"Independent Learning of Nash Equilibria in Partially Observable Markov Potential Games with Decoupled Dynamics","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-05-08T04:21:21.394989Z"},"links":{"citing_paper":"/paper/2605.06377"},"observation_digest":"sha256:0ceb9b18733ae9eb552238fe4e9c8e1191f85f622d65179293e66335e4c8a346","observation_id":"c81aaa2d-25d0-4ddd-972f-4ee1ebb6015b","resolution":{"observed_at":"2026-05-26T21:08:03.260428Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-09T14:16:17.500145Z","title":"Near-optimal reinforcement learning in polynomial time","venue":null,"work_id":"6d066ecf-f46e-4751-a5f1-e83272b1dbf6","year":2002},"citing_paper":{"arxiv_id":"2605.06377","last_updated":"2026-05-07T14:56:56Z","snapshot_observed_at":"2026-07-06T23:18:51.157048Z","submitted_at":"2026-05-07T14:56:56Z","title":"Independent Learning of Nash Equilibria in Partially Observable Markov Potential Games with Decoupled Dynamics","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-05-08T04:21:21.394989Z"},"links":{"citing_paper":"/paper/2605.06377"},"observation_digest":"sha256:5ec41711ba6f142ed30c4b3b7d8c00cca2c5ce97d48726b6230067c20d421724","observation_id":"70a1808d-fd33-49bf-8a13-c67e67116b09","resolution":{"observed_at":"2026-05-26T21:08:03.286792Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"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","venue":null,"work_id":"c4935f90-948c-4fc4-910e-fe6760ec4d65","year":2021},"citing_paper":{"arxiv_id":"2605.06377","last_updated":"2026-05-07T14:56:56Z","snapshot_observed_at":"2026-07-06T23:18:51.157048Z","submitted_at":"2026-05-07T14:56:56Z","title":"Independent Learning of Nash Equilibria in Partially Observable Markov Potential Games with Decoupled Dynamics","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-05-08T04:21:21.394989Z"},"links":{"citing_paper":"/paper/2605.06377"},"observation_digest":"sha256:7b4056cd8624901f2880b41477d038fe3a5af35a79f828123eb9af9dedc54409","observation_id":"fe969516-8ebf-4c36-b2d3-498f585aa89e","resolution":{"observed_at":"2026-05-26T21:08:03.246122Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2605.06377","last_updated":"2026-05-07T14:56:56Z","latest_version":1,"primary_category":"cs.GT","snapshot_observed_at":"2026-07-06T23:18:51.157048Z","submitted_at":"2026-05-07T14:56:56Z","title":"Independent Learning of Nash Equilibria in Partially Observable Markov Potential Games with Decoupled Dynamics"},"reference_resolution":{"displayed":49,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":1,"verified_fuzzy":48},"total_outbound_references":49},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"thesis":"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."}