{"as_of":"2026-08-16T23:43:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:3d5a8e41eae2f8b5c0c261467fc9a8d87332eb3161eaa3db6207de8c6003eef1","coverage":[{"denominator":37,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":37,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-14T13:16:22.503217Z","state":"measured"},{"denominator":37,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":37,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-16T06:30:59.297886+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/1908.05451/citation-record","integrity":"/paper/1908.05451/integrity","json":"/paper/1908.05451/citation-record.json","paper":"/paper/1908.05451"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T13:16:22.997179Z","title":"Efficient memory-based learning for robot control","venue":null,"work_id":"b18e42db-ff10-40e1-8483-29780d11b04b","year":1990},"citing_paper":{"arxiv_id":"1908.05451","last_updated":"2019-08-15T08:01:56Z","snapshot_observed_at":"2026-08-15T22:52:21.255383Z","submitted_at":"2019-08-15T08:01:56Z","title":"Mapping State Space using Landmarks for Universal Goal Reaching","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-14T13:16:22.215278Z"},"links":{"citing_paper":"/paper/1908.05451"},"observation_digest":"sha256:74e3c28fd586ee6d5450f4ee60be1214a7d6be363ae449c405170a3c9e25732c","observation_id":"9e2e0796-ac37-4b45-a005-ae9ee557f52a","resolution":{"observed_at":"2026-08-14T13:16:23.001178Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1805.08180","last_updated":"2019-03-08T17:52:47Z","snapshot_observed_at":"2026-07-06T06:40:22.436755Z","submitted_at":"2018-05-21T17:02:53Z","title":"Hierarchical Reinforcement Learning with Hindsight","version":2},"cited_work":{"arxiv_id":"1805.08180","doi":null,"metadata_source":"pith","pith_arxiv_id":"1805.08180","snapshot_observed_at":"2026-08-14T13:16:22.726237Z","title":"Hierarchical Reinforcement Learning with Hindsight","venue":"cs.LG","work_id":"0eca5201-369c-41d4-87ff-a788e566f6fd","year":2018},"citing_paper":{"arxiv_id":"1908.05451","last_updated":"2019-08-15T08:01:56Z","snapshot_observed_at":"2026-08-15T22:52:21.255383Z","submitted_at":"2019-08-15T08:01:56Z","title":"Mapping State Space using Landmarks for Universal Goal Reaching","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-14T13:16:22.220192Z"},"links":{"cited_paper":"/paper/1805.08180","citing_paper":"/paper/1908.05451"},"observation_digest":"sha256:4a9b7b3bb567a8edb7d990b02fb90305e566bea451b32cb69990af8ba107ad84","observation_id":"82c48303-614f-498d-8e0b-e2c75c825440","resolution":{"observed_at":"2026-08-14T13:16:22.730738Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T13:16:22.984043Z","title":"Universal value function approxi- mators","venue":null,"work_id":"c9b8ec6e-b997-4ffe-867b-44bfd90da6fa","year":2015},"citing_paper":{"arxiv_id":"1908.05451","last_updated":"2019-08-15T08:01:56Z","snapshot_observed_at":"2026-08-15T22:52:21.255383Z","submitted_at":"2019-08-15T08:01:56Z","title":"Mapping State Space using Landmarks for Universal Goal Reaching","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-14T13:16:22.224901Z"},"links":{"citing_paper":"/paper/1908.05451"},"observation_digest":"sha256:c517626d77d2882577c1841e42bc6a9fba21fa2fbaf06b2935dda306d1e26d7a","observation_id":"d786f4d2-f66a-4f0e-95b3-5332842228f4","resolution":{"observed_at":"2026-08-14T13:16:22.988991Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T13:16:22.229427Z","title":"Hindsight experience replay","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"1908.05451","last_updated":"2019-08-15T08:01:56Z","snapshot_observed_at":"2026-08-15T22:52:21.255383Z","submitted_at":"2019-08-15T08:01:56Z","title":"Mapping State Space using Landmarks for Universal Goal Reaching","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-14T13:16:22.229427Z"},"links":{"citing_paper":"/paper/1908.05451"},"observation_digest":"sha256:f3456bff18e49a180be105c90b9e62c6b3dd40d6e7f368c11e5d8e8d7f8e5eff","observation_id":"f2b8d0f0-09ef-4354-b476-46a8dcb6b77d","resolution":{"observed_at":"2026-08-14T13:16:22.229427Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T13:16:22.959363Z","title":null,"venue":null,"work_id":"e2b1fb39-6aa1-4e27-acfc-d8a91016866e","year":2004},"citing_paper":{"arxiv_id":"1908.05451","last_updated":"2019-08-15T08:01:56Z","snapshot_observed_at":"2026-08-15T22:52:21.255383Z","submitted_at":"2019-08-15T08:01:56Z","title":"Mapping State Space using Landmarks for Universal Goal Reaching","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-14T13:16:22.233626Z"},"links":{"citing_paper":"/paper/1908.05451"},"observation_digest":"sha256:291a202654d45b4de9a81fe627c28503124a32ef1758c66d902874833fcc40f4","observation_id":"946c7bd1-499b-467e-b6a4-21ffdfb2904d","resolution":{"observed_at":"2026-08-14T13:16:22.964490Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T13:16:22.946103Z","title":"Sparse multidimensional scaling using landmark points","venue":null,"work_id":"8b4ea0aa-812e-403e-9a6e-fb3536d58c05","year":2004},"citing_paper":{"arxiv_id":"1908.05451","last_updated":"2019-08-15T08:01:56Z","snapshot_observed_at":"2026-08-15T22:52:21.255383Z","submitted_at":"2019-08-15T08:01:56Z","title":"Mapping State Space using Landmarks for Universal Goal Reaching","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-14T13:16:22.237786Z"},"links":{"citing_paper":"/paper/1908.05451"},"observation_digest":"sha256:5003e4c2857fa482f32d2c62f7a0e52c5e857df8f30e045616faad3e61abf926","observation_id":"09703a54-34f9-46a4-b40c-26b62b50d195","resolution":{"observed_at":"2026-08-14T13:16:22.950291Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T13:16:22.932477Z","title":"Horde: A scalable real-time architecture for learning knowledge from unsupervised sensorimotor interaction","venue":null,"work_id":"68fddf3a-f4c6-450e-a0b3-c0cb247718df","year":2011},"citing_paper":{"arxiv_id":"1908.05451","last_updated":"2019-08-15T08:01:56Z","snapshot_observed_at":"2026-08-15T22:52:21.255383Z","submitted_at":"2019-08-15T08:01:56Z","title":"Mapping State Space using Landmarks for Universal Goal Reaching","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-14T13:16:22.241876Z"},"links":{"citing_paper":"/paper/1908.05451"},"observation_digest":"sha256:ea83bae9216a48ef133d9445b2979698a8a41b3c35a2628be58a979fae17fc81","observation_id":"ec0593d5-b0dc-4991-8cf4-e93bc49efa38","resolution":{"observed_at":"2026-08-14T13:16:22.936749Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T13:16:22.918452Z","title":"Universal agent for disentangling environments and tasks","venue":null,"work_id":"39b17427-d2a1-4c3c-88a1-6da662cffa91","year":2018},"citing_paper":{"arxiv_id":"1908.05451","last_updated":"2019-08-15T08:01:56Z","snapshot_observed_at":"2026-08-15T22:52:21.255383Z","submitted_at":"2019-08-15T08:01:56Z","title":"Mapping State Space using Landmarks for Universal Goal Reaching","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-14T13:16:22.245869Z"},"links":{"citing_paper":"/paper/1908.05451"},"observation_digest":"sha256:62ac8f3e4502d67feea7b385356ab731566bc920d73841868da1b477c16b5409","observation_id":"5ad2c7e8-4ddd-4198-a2cf-f83f0fd3fb5f","resolution":{"observed_at":"2026-08-14T13:16:22.923073Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1802.09081","last_updated":"2020-02-24T06:34:11Z","snapshot_observed_at":"2026-08-14T19:42:22.944834Z","submitted_at":"2018-02-25T21:14:44Z","title":"Temporal Difference Models: Model-Free Deep RL for Model-Based Control","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1802.09081","snapshot_observed_at":"2026-08-14T13:16:22.249705Z","title":"Temporal difference models: Model-free deep RL for model-based control","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"1908.05451","last_updated":"2019-08-15T08:01:56Z","snapshot_observed_at":"2026-08-15T22:52:21.255383Z","submitted_at":"2019-08-15T08:01:56Z","title":"Mapping State Space using Landmarks for Universal Goal Reaching","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-14T13:16:22.249705Z"},"links":{"cited_paper":"/paper/1802.09081","citing_paper":"/paper/1908.05451"},"observation_digest":"sha256:789b3d8fec038a12e9c3a7d65a9fc9701c5cbd49a092afa033f6bb88933b91f5","observation_id":"d1dc4fab-bde4-4949-b068-1a063cbeee08","resolution":{"observed_at":"2026-08-14T13:16:22.249705Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1509.02971","last_updated":"2019-07-05T10:47:27Z","snapshot_observed_at":"2026-08-16T22:06:26.835611Z","submitted_at":"2015-09-09T23:01:36Z","title":"Continuous control with deep reinforcement learning","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1509.02971","snapshot_observed_at":"2026-08-14T13:16:22.253706Z","title":"Continuous control with deep reinforcement learning","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"1908.05451","last_updated":"2019-08-15T08:01:56Z","snapshot_observed_at":"2026-08-15T22:52:21.255383Z","submitted_at":"2019-08-15T08:01:56Z","title":"Mapping State Space using Landmarks for Universal Goal Reaching","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-14T13:16:22.253706Z"},"links":{"cited_paper":"/paper/1509.02971","citing_paper":"/paper/1908.05451"},"observation_digest":"sha256:8c3d3acb7cb57cfea9a7b753999b12c6faa7cb09869c863f269385a111d58b77","observation_id":"5048dc90-f5d5-4174-b66b-424baaa5aecf","resolution":{"observed_at":"2026-08-14T13:16:22.253706Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1811.04551","last_updated":"2019-06-04T18:13:09Z","snapshot_observed_at":"2026-08-14T18:00:53.874885Z","submitted_at":"2018-11-12T04:30:10Z","title":"Learning Latent Dynamics for Planning from Pixels","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1811.04551","snapshot_observed_at":"2026-08-14T13:16:22.257797Z","title":"Learning latent dynamics for planning from pixels","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"1908.05451","last_updated":"2019-08-15T08:01:56Z","snapshot_observed_at":"2026-08-15T22:52:21.255383Z","submitted_at":"2019-08-15T08:01:56Z","title":"Mapping State Space using Landmarks for Universal Goal Reaching","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-14T13:16:22.257797Z"},"links":{"cited_paper":"/paper/1811.04551","citing_paper":"/paper/1908.05451"},"observation_digest":"sha256:d41ace094f49dea2716c9b40b896b43350685e9fb7ca69fca14c355fa1f6b13e","observation_id":"b7e443ee-1edc-48b3-b54d-6382a538640b","resolution":{"observed_at":"2026-08-14T13:16:22.257797Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T13:16:22.905074Z","title":"Value prediction network","venue":null,"work_id":"92f28199-96d6-42ec-b582-832a39ae1a41","year":2017},"citing_paper":{"arxiv_id":"1908.05451","last_updated":"2019-08-15T08:01:56Z","snapshot_observed_at":"2026-08-15T22:52:21.255383Z","submitted_at":"2019-08-15T08:01:56Z","title":"Mapping State Space using Landmarks for Universal Goal Reaching","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-14T13:16:22.263483Z"},"links":{"citing_paper":"/paper/1908.05451"},"observation_digest":"sha256:3ef4b767d6f46dce3390069ef879c78ef19bbcc301034a879726c376fddec5ab","observation_id":"07e9d644-f6f6-4ac9-b21a-8c440b473dab","resolution":{"observed_at":"2026-08-14T13:16:22.909576Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T13:16:22.892457Z","title":null,"venue":null,"work_id":"2eebfa4a-3d63-429b-a334-5c2fd258e98f","year":2016},"citing_paper":{"arxiv_id":"1908.05451","last_updated":"2019-08-15T08:01:56Z","snapshot_observed_at":"2026-08-15T22:52:21.255383Z","submitted_at":"2019-08-15T08:01:56Z","title":"Mapping State Space using Landmarks for Universal Goal Reaching","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-14T13:16:22.268185Z"},"links":{"citing_paper":"/paper/1908.05451"},"observation_digest":"sha256:acba28c203ff30198bb102f00396634c25ac2cfb6f59db33313efb20f7c0fa38","observation_id":"d0db6fe5-ccfa-4516-ba7d-7ec67d930a16","resolution":{"observed_at":"2026-08-14T13:16:22.896790Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1705.07177","last_updated":"2018-04-04T06:34:26Z","snapshot_observed_at":"2026-08-14T20:59:27.523648Z","submitted_at":"2017-05-19T20:38:49Z","title":"Model-Based Planning with Discrete and Continuous Actions","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1705.07177","snapshot_observed_at":"2026-08-14T13:16:22.273307Z","title":"Model-based planning with discrete and continuous actions","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"1908.05451","last_updated":"2019-08-15T08:01:56Z","snapshot_observed_at":"2026-08-15T22:52:21.255383Z","submitted_at":"2019-08-15T08:01:56Z","title":"Mapping State Space using Landmarks for Universal Goal Reaching","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-14T13:16:22.273307Z"},"links":{"cited_paper":"/paper/1705.07177","citing_paper":"/paper/1908.05451"},"observation_digest":"sha256:3ceaf39562f06517ab90debad20cc3f79bb32dc4bdcb1095aba4dcf03ca7c475","observation_id":"07e477f6-8fd6-47a0-b7f8-fee9cf35ec1b","resolution":{"observed_at":"2026-08-14T13:16:22.273307Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1804.00645","last_updated":"2018-04-04T17:36:36Z","snapshot_observed_at":"2026-08-15T00:38:32.751888Z","submitted_at":"2018-04-02T17:51:53Z","title":"Universal Planning Networks","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1804.00645","snapshot_observed_at":"2026-08-14T13:16:22.278636Z","title":"Universal planning networks","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"1908.05451","last_updated":"2019-08-15T08:01:56Z","snapshot_observed_at":"2026-08-15T22:52:21.255383Z","submitted_at":"2019-08-15T08:01:56Z","title":"Mapping State Space using Landmarks for Universal Goal Reaching","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-14T13:16:22.278636Z"},"links":{"cited_paper":"/paper/1804.00645","citing_paper":"/paper/1908.05451"},"observation_digest":"sha256:86932f423c988f31ddf19058fc0703556f776691c1d54aa16cf42573ab00bb9e","observation_id":"2fde6be5-179e-4b14-8aef-82b544017712","resolution":{"observed_at":"2026-08-14T13:16:22.278636Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1902.05542","last_updated":"2019-02-14T18:54:54Z","snapshot_observed_at":"2026-08-14T17:16:13.959306Z","submitted_at":"2019-02-14T18:54:54Z","title":"Unsupervised Visuomotor Control through Distributional Planning Networks","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1902.05542","snapshot_observed_at":"2026-08-14T13:16:22.283258Z","title":"Unsupervised visuomotor control through distributional planning networks","venue":null,"work_id":null,"year":1902},"citing_paper":{"arxiv_id":"1908.05451","last_updated":"2019-08-15T08:01:56Z","snapshot_observed_at":"2026-08-15T22:52:21.255383Z","submitted_at":"2019-08-15T08:01:56Z","title":"Mapping State Space using Landmarks for Universal Goal Reaching","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-14T13:16:22.283258Z"},"links":{"cited_paper":"/paper/1902.05542","citing_paper":"/paper/1908.05451"},"observation_digest":"sha256:797cdd27cc67f2c65c82c263b6b2de91c1cfe46c2a2d55cb05c201b1b5afda45","observation_id":"5ac9d929-707c-4d4e-b909-e868a6347a27","resolution":{"observed_at":"2026-08-14T13:16:22.283258Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T13:16:22.878733Z","title":"Modeling the long term future in model-based reinforcement learning","venue":null,"work_id":"a81e8ece-a414-406a-9fb7-46fe9a8b70c7","year":2018},"citing_paper":{"arxiv_id":"1908.05451","last_updated":"2019-08-15T08:01:56Z","snapshot_observed_at":"2026-08-15T22:52:21.255383Z","submitted_at":"2019-08-15T08:01:56Z","title":"Mapping State Space using Landmarks for Universal Goal Reaching","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-14T13:16:22.288803Z"},"links":{"citing_paper":"/paper/1908.05451"},"observation_digest":"sha256:458180adf4783d2fb858d6a7e5073071bc34860aec999954741aeb4ecd7ba9c4","observation_id":"db53c5c4-472b-4f0e-a75c-9c20c16ecc1b","resolution":{"observed_at":"2026-08-14T13:16:22.883439Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T13:16:22.865220Z","title":"Value iteration networks","venue":null,"work_id":"b2791f58-a894-4e8d-b843-488a32926c77","year":2016},"citing_paper":{"arxiv_id":"1908.05451","last_updated":"2019-08-15T08:01:56Z","snapshot_observed_at":"2026-08-15T22:52:21.255383Z","submitted_at":"2019-08-15T08:01:56Z","title":"Mapping State Space using Landmarks for Universal Goal Reaching","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-14T13:16:22.292900Z"},"links":{"citing_paper":"/paper/1908.05451"},"observation_digest":"sha256:c6a0a03ef14169b6e22f82c274ff1d1c0df060ef28ccc230280f461ae166c864","observation_id":"f0c84fb4-a276-4951-837e-13a5b4decd30","resolution":{"observed_at":"2026-08-14T13:16:22.869300Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T13:16:22.296869Z","title":"Gradient-based learning applied to document recognition","venue":null,"work_id":null,"year":1998},"citing_paper":{"arxiv_id":"1908.05451","last_updated":"2019-08-15T08:01:56Z","snapshot_observed_at":"2026-08-15T22:52:21.255383Z","submitted_at":"2019-08-15T08:01:56Z","title":"Mapping State Space using Landmarks for Universal Goal Reaching","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-14T13:16:22.296869Z"},"links":{"citing_paper":"/paper/1908.05451"},"observation_digest":"sha256:805ee2d178dcfb2f21d584f7b1f5cfe959e4f480352543a5b57aaca2dd7ee6f8","observation_id":"f52405c0-7606-4423-9990-04c73cb8426c","resolution":{"observed_at":"2026-08-14T13:16:22.296869Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T13:16:22.843896Z","title":"A formal basis for the heuristic determination of minimum cost paths","venue":null,"work_id":"fe8c52c5-4358-475c-ae9b-442741e3832e","year":1968},"citing_paper":{"arxiv_id":"1908.05451","last_updated":"2019-08-15T08:01:56Z","snapshot_observed_at":"2026-08-15T22:52:21.255383Z","submitted_at":"2019-08-15T08:01:56Z","title":"Mapping State Space using Landmarks for Universal Goal Reaching","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-14T13:16:22.429395Z"},"links":{"citing_paper":"/paper/1908.05451"},"observation_digest":"sha256:4439feea5f4b8785a4c63536a3758a4c3a01c97ff274334966806f9b02388e2b","observation_id":"90dabf1b-8da3-4b23-bdaf-c709999edb0b","resolution":{"observed_at":"2026-08-14T13:16:22.848085Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T13:16:22.831501Z","title":"Rapidly-exploring random trees: A new tool for path planning","venue":null,"work_id":"fe1131bb-9ab5-415c-b145-2da2f6bcee07","year":1998},"citing_paper":{"arxiv_id":"1908.05451","last_updated":"2019-08-15T08:01:56Z","snapshot_observed_at":"2026-08-15T22:52:21.255383Z","submitted_at":"2019-08-15T08:01:56Z","title":"Mapping State Space using Landmarks for Universal Goal Reaching","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-14T13:16:22.433369Z"},"links":{"citing_paper":"/paper/1908.05451"},"observation_digest":"sha256:812634296375f57c1f996169fc2460af415e6b7060ac651fb76ef80300a6bc6a","observation_id":"b5f2a9b6-cc29-4d26-8a89-69b1c5a07e93","resolution":{"observed_at":"2026-08-14T13:16:22.836189Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T13:16:22.817303Z","title":"Probabilistic roadmaps for path planning in high-dimensional configuration spaces, volume 1994","venue":null,"work_id":"ce67344c-a539-409b-9969-a2232894dcea","year":1994},"citing_paper":{"arxiv_id":"1908.05451","last_updated":"2019-08-15T08:01:56Z","snapshot_observed_at":"2026-08-15T22:52:21.255383Z","submitted_at":"2019-08-15T08:01:56Z","title":"Mapping State Space using Landmarks for Universal Goal Reaching","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-14T13:16:22.437279Z"},"links":{"citing_paper":"/paper/1908.05451"},"observation_digest":"sha256:71226973083dbf75ab9403e31bdb70088e1d5c66b2a5331a38125951e5b3ddfe","observation_id":"26f341e6-ac7e-4232-93df-5a1c1bff63f0","resolution":{"observed_at":"2026-08-14T13:16:22.822562Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1807.10366","last_updated":"2018-11-06T15:47:59Z","snapshot_observed_at":"2026-08-14T18:47:16.864944Z","submitted_at":"2018-07-26T21:11:26Z","title":"Robot Motion Planning in Learned Latent Spaces","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1807.10366","snapshot_observed_at":"2026-08-14T13:16:22.441074Z","title":"Robot motion planning in learned latent spaces","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"1908.05451","last_updated":"2019-08-15T08:01:56Z","snapshot_observed_at":"2026-08-15T22:52:21.255383Z","submitted_at":"2019-08-15T08:01:56Z","title":"Mapping State Space using Landmarks for Universal Goal Reaching","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-14T13:16:22.441074Z"},"links":{"cited_paper":"/paper/1807.10366","citing_paper":"/paper/1908.05451"},"observation_digest":"sha256:faf86c121624bec10da4437c6fd964bc5ff63c50aaeaaa27357db4d3b79df579","observation_id":"62f0b633-45c5-4c45-93be-1a194a5d94bc","resolution":{"observed_at":"2026-08-14T13:16:22.441074Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1809.10252","last_updated":"2018-09-26T22:14:55Z","snapshot_observed_at":"2026-08-14T18:23:01.551319Z","submitted_at":"2018-09-26T22:14:55Z","title":"Deeply Informed Neural Sampling for Robot Motion Planning","version":1},"cited_work":{"arxiv_id":"1809.10252","doi":null,"metadata_source":"pith","pith_arxiv_id":"1809.10252","snapshot_observed_at":"2026-08-14T13:16:22.628707Z","title":"Deeply Informed Neural Sampling for Robot Motion Planning","venue":"cs.RO","work_id":"9ab8b341-bd5e-42a5-8223-703c7297d2a2","year":2018},"citing_paper":{"arxiv_id":"1908.05451","last_updated":"2019-08-15T08:01:56Z","snapshot_observed_at":"2026-08-15T22:52:21.255383Z","submitted_at":"2019-08-15T08:01:56Z","title":"Mapping State Space using Landmarks for Universal Goal Reaching","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-14T13:16:22.446071Z"},"links":{"cited_paper":"/paper/1809.10252","citing_paper":"/paper/1908.05451"},"observation_digest":"sha256:bfd3c47cbc9449a6e3c7c1c5b8667d86ed442a8e54cf9665704d11ecc8fa65b7","observation_id":"94d70fe2-53b3-4127-a46b-1d634c5517d9","resolution":{"observed_at":"2026-08-14T13:16:22.633003Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1903.02308","last_updated":"2019-03-06T10:55:31Z","snapshot_observed_at":"2026-08-14T17:06:38.370735Z","submitted_at":"2019-03-06T10:55:31Z","title":"Towards Learning Abstract Representations for Locomotion Planning in High-dimensional State Spaces","version":1},"cited_work":{"arxiv_id":"1903.02308","doi":null,"metadata_source":"pith","pith_arxiv_id":"1903.02308","snapshot_observed_at":"2026-08-14T13:16:22.611651Z","title":"Towards Learning Abstract Representations for Locomotion Planning in High-dimensional State Spaces","venue":"cs.RO","work_id":"ab883679-bf9e-4470-b09f-ddd3414f1e9b","year":2019},"citing_paper":{"arxiv_id":"1908.05451","last_updated":"2019-08-15T08:01:56Z","snapshot_observed_at":"2026-08-15T22:52:21.255383Z","submitted_at":"2019-08-15T08:01:56Z","title":"Mapping State Space using Landmarks for Universal Goal Reaching","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-14T13:16:22.450480Z"},"links":{"cited_paper":"/paper/1903.02308","citing_paper":"/paper/1908.05451"},"observation_digest":"sha256:a60c9a7da5858e5afed9b7184e0eb312a677a1a4183c490bc832a5aaa4382c85","observation_id":"e2b0fcc0-3bc7-4280-b3ed-79830212e0c2","resolution":{"observed_at":"2026-08-14T13:16:22.616387Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T13:16:22.804920Z","title":"Prm-rl: Long-range robotic navigation tasks by combining reinforcement learning and sampling-based planning","venue":null,"work_id":"e798b0ce-f853-4358-932f-508023948d83","year":2018},"citing_paper":{"arxiv_id":"1908.05451","last_updated":"2019-08-15T08:01:56Z","snapshot_observed_at":"2026-08-15T22:52:21.255383Z","submitted_at":"2019-08-15T08:01:56Z","title":"Mapping State Space using Landmarks for Universal Goal Reaching","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-14T13:16:22.455382Z"},"links":{"citing_paper":"/paper/1908.05451"},"observation_digest":"sha256:9af699ee950693b99bce7c0dc48df835b75d88f4d41ccd9d73b2aae17db69b4b","observation_id":"84ef40d1-21de-4324-a013-b59ee8826aa8","resolution":{"observed_at":"2026-08-14T13:16:22.809055Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1803.00653","last_updated":"2018-03-01T22:50:35Z","snapshot_observed_at":"2026-08-14T19:40:28.332397Z","submitted_at":"2018-03-01T22:50:35Z","title":"Semi-parametric Topological Memory for Navigation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1803.00653","snapshot_observed_at":"2026-08-14T13:16:22.459812Z","title":"Semi-parametric topological memory for navigation","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"1908.05451","last_updated":"2019-08-15T08:01:56Z","snapshot_observed_at":"2026-08-15T22:52:21.255383Z","submitted_at":"2019-08-15T08:01:56Z","title":"Mapping State Space using Landmarks for Universal Goal Reaching","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-14T13:16:22.459812Z"},"links":{"cited_paper":"/paper/1803.00653","citing_paper":"/paper/1908.05451"},"observation_digest":"sha256:765147bb0179788a830ec70867e949392e864ac31971826ded5e7375b176504e","observation_id":"1c2ede4b-480d-420f-8918-f26e3c671f95","resolution":{"observed_at":"2026-08-14T13:16:22.459812Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1803.00512","last_updated":"2019-04-25T20:14:27Z","snapshot_observed_at":"2026-08-14T19:40:35.344740Z","submitted_at":"2018-03-01T17:21:03Z","title":"Composable Planning with Attributes","version":2},"cited_work":{"arxiv_id":"1803.00512","doi":null,"metadata_source":"pith","pith_arxiv_id":"1803.00512","snapshot_observed_at":"2026-08-14T13:16:22.574773Z","title":"Composable Planning with Attributes","venue":"cs.AI","work_id":"30d5244d-9c67-43b6-a808-ed835ec43da7","year":2018},"citing_paper":{"arxiv_id":"1908.05451","last_updated":"2019-08-15T08:01:56Z","snapshot_observed_at":"2026-08-15T22:52:21.255383Z","submitted_at":"2019-08-15T08:01:56Z","title":"Mapping State Space using Landmarks for Universal Goal Reaching","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-14T13:16:22.464569Z"},"links":{"cited_paper":"/paper/1803.00512","citing_paper":"/paper/1908.05451"},"observation_digest":"sha256:6a609caadd381c89742227248b0efcacbd830decf43128c5937d7310f00b2798","observation_id":"eacc1d40-a780-43ad-8b9e-12959fa1bd0e","resolution":{"observed_at":"2026-08-14T13:16:22.582060Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T13:16:22.793195Z","title":"Hierarchical deep reinforcement learning: Integrating temporal abstraction and intrinsic motivation","venue":null,"work_id":"e3ea54ec-8a02-4615-b37a-92328e7dca44","year":2016},"citing_paper":{"arxiv_id":"1908.05451","last_updated":"2019-08-15T08:01:56Z","snapshot_observed_at":"2026-08-15T22:52:21.255383Z","submitted_at":"2019-08-15T08:01:56Z","title":"Mapping State Space using Landmarks for Universal Goal Reaching","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-14T13:16:22.468713Z"},"links":{"citing_paper":"/paper/1908.05451"},"observation_digest":"sha256:81a1539be198aa36ab57c9dd2f3793ec7379a9a2580606fb17835e050eeb82b9","observation_id":"ea2518ea-6d4a-4671-835d-4bb1558e7b44","resolution":{"observed_at":"2026-08-14T13:16:22.797433Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T13:16:22.779880Z","title":"Data-efficient hierarchical reinforcement learning","venue":null,"work_id":"9d2b1636-9a5b-498e-8698-931d7e0b3fd2","year":2018},"citing_paper":{"arxiv_id":"1908.05451","last_updated":"2019-08-15T08:01:56Z","snapshot_observed_at":"2026-08-15T22:52:21.255383Z","submitted_at":"2019-08-15T08:01:56Z","title":"Mapping State Space using Landmarks for Universal Goal Reaching","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-14T13:16:22.472809Z"},"links":{"citing_paper":"/paper/1908.05451"},"observation_digest":"sha256:15f7ccdc4194ac020c1ef320227d48681e4e10cc4a023feaafbc45725cd38809","observation_id":"c59384b6-c40e-4f0e-ac74-4bab04a4a568","resolution":{"observed_at":"2026-08-14T13:16:22.785340Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1604.06778","last_updated":"2016-05-27T19:25:59Z","snapshot_observed_at":"2026-08-14T22:00:28.306146Z","submitted_at":"2016-04-22T18:57:24Z","title":"Benchmarking Deep Reinforcement Learning for Continuous Control","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1604.06778","snapshot_observed_at":"2026-08-14T13:16:22.477055Z","title":"Benchmarking deep reinforcement learning for continuous control","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"1908.05451","last_updated":"2019-08-15T08:01:56Z","snapshot_observed_at":"2026-08-15T22:52:21.255383Z","submitted_at":"2019-08-15T08:01:56Z","title":"Mapping State Space using Landmarks for Universal Goal Reaching","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-14T13:16:22.477055Z"},"links":{"cited_paper":"/paper/1604.06778","citing_paper":"/paper/1908.05451"},"observation_digest":"sha256:2104c7c6496f99ed860f657f008f33a4cd426aef9964abbfe7561a5e1cad60c7","observation_id":"65e63715-408f-46ac-8106-7c3e4769e928","resolution":{"observed_at":"2026-08-14T13:16:22.477055Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1802.09464","last_updated":"2018-03-10T18:11:25Z","snapshot_observed_at":"2026-08-14T19:42:01.790871Z","submitted_at":"2018-02-26T17:20:14Z","title":"Multi-Goal Reinforcement Learning: Challenging Robotics Environments and Request for Research","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1802.09464","snapshot_observed_at":"2026-08-14T13:16:22.480974Z","title":"Multi-goal reinforcement learning: Challenging robotics environments and request for research","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"1908.05451","last_updated":"2019-08-15T08:01:56Z","snapshot_observed_at":"2026-08-15T22:52:21.255383Z","submitted_at":"2019-08-15T08:01:56Z","title":"Mapping State Space using Landmarks for Universal Goal Reaching","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-14T13:16:22.480974Z"},"links":{"cited_paper":"/paper/1802.09464","citing_paper":"/paper/1908.05451"},"observation_digest":"sha256:94b442fafdc86d8c1b6caab3d853c23868d2b10ae3094cb29ece69f73c2a757b","observation_id":"6f872bb6-8932-4748-abe7-2550875d7c9b","resolution":{"observed_at":"2026-08-14T13:16:22.480974Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T13:16:22.767487Z","title":"Computing the shortest path: A search meets graph theory","venue":null,"work_id":"dc5823ae-bc86-4c9e-a0ee-c4b538c0f41a","year":2005},"citing_paper":{"arxiv_id":"1908.05451","last_updated":"2019-08-15T08:01:56Z","snapshot_observed_at":"2026-08-15T22:52:21.255383Z","submitted_at":"2019-08-15T08:01:56Z","title":"Mapping State Space using Landmarks for Universal Goal Reaching","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-14T13:16:22.485136Z"},"links":{"citing_paper":"/paper/1908.05451"},"observation_digest":"sha256:9c44c8ef5ae5b081fbcde09bd9fca61520660f1106afc8dbd92b37c67a5cf507","observation_id":"2951b6ff-bbe0-4fca-8628-84bcb749f63d","resolution":{"observed_at":"2026-08-14T13:16:22.771645Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T13:16:22.488871Z","title":"k-means++: The advantages of careful seeding","venue":null,"work_id":null,"year":2007},"citing_paper":{"arxiv_id":"1908.05451","last_updated":"2019-08-15T08:01:56Z","snapshot_observed_at":"2026-08-15T22:52:21.255383Z","submitted_at":"2019-08-15T08:01:56Z","title":"Mapping State Space using Landmarks for Universal Goal Reaching","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-14T13:16:22.488871Z"},"links":{"citing_paper":"/paper/1908.05451"},"observation_digest":"sha256:6c2a58229b083818f5c567d4846202983f81a5dcf2d0dc33428d6386fa1ab3ca","observation_id":"9694827b-a04d-4554-90cf-5e87f4c4220e","resolution":{"observed_at":"2026-08-14T13:16:22.488871Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1312.5602","last_updated":"2013-12-19T16:00:08Z","snapshot_observed_at":"2026-08-14T03:19:40.736445Z","submitted_at":"2013-12-19T16:00:08Z","title":"Playing Atari with Deep Reinforcement Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1312.5602","snapshot_observed_at":"2026-08-14T13:16:22.494165Z","title":"Riedmiller","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"1908.05451","last_updated":"2019-08-15T08:01:56Z","snapshot_observed_at":"2026-08-15T22:52:21.255383Z","submitted_at":"2019-08-15T08:01:56Z","title":"Mapping State Space using Landmarks for Universal Goal Reaching","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-14T13:16:22.494165Z"},"links":{"cited_paper":"/paper/1312.5602","citing_paper":"/paper/1908.05451"},"observation_digest":"sha256:1db15d51a567bcc58a8807f6a9877a668153bf5b65733ed98a56492fb84fc0b8","observation_id":"392b43b0-7041-44bc-bade-48e76904c61a","resolution":{"observed_at":"2026-08-14T13:16:22.494165Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T13:16:22.498838Z","title":"Mujoco: A physics engine for model-based control","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"1908.05451","last_updated":"2019-08-15T08:01:56Z","snapshot_observed_at":"2026-08-15T22:52:21.255383Z","submitted_at":"2019-08-15T08:01:56Z","title":"Mapping State Space using Landmarks for Universal Goal Reaching","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-14T13:16:22.498838Z"},"links":{"citing_paper":"/paper/1908.05451"},"observation_digest":"sha256:023434cd25c825052797e9fbee8202add8a7acd7b6c3fc25f284ee0f3e8a2f7d","observation_id":"061550b6-6102-46dd-8c52-c1e0489d682f","resolution":{"observed_at":"2026-08-14T13:16:22.498838Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T13:16:22.738654Z","title":"Openai gym, 2016","venue":null,"work_id":"e3156bd7-46f6-4f4f-98f1-7f1de17dc5db","year":2016},"citing_paper":{"arxiv_id":"1908.05451","last_updated":"2019-08-15T08:01:56Z","snapshot_observed_at":"2026-08-15T22:52:21.255383Z","submitted_at":"2019-08-15T08:01:56Z","title":"Mapping State Space using Landmarks for Universal Goal Reaching","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-14T13:16:22.503217Z"},"links":{"citing_paper":"/paper/1908.05451"},"observation_digest":"sha256:5e37a275b63939519ff5e792b70b99760a40c2f184db94dea5a3bf62aebf74b0","observation_id":"46610cca-ae10-4c7b-9e8f-59dc62ffc71a","resolution":{"observed_at":"2026-08-14T13:16:22.742440Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"1908.05451","last_updated":"2019-08-15T08:01:56Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-15T22:52:21.255383Z","submitted_at":"2019-08-15T08:01:56Z","title":"Mapping State Space using Landmarks for Universal Goal Reaching"},"reference_resolution":{"displayed":37,"state_counts":{"malformed_identifier":0,"metadata_mismatch":1,"parse_uncertain":0,"unresolved":17,"verified_exact":3,"verified_fuzzy":16},"total_outbound_references":37},"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":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"thesis":"As of 16 August 2026, this Paper Citation Record lists 37 of 37 outbound references and 0 inbound Pith citation observations for arXiv:1908.05451."}