{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:4JZAAIW5LNS7CVDR34SRSVHPOH","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"a61d6feac87f8846573097a153beec41c7fb211d389d43c4a03ca2340666f03a","cross_cats_sorted":["cs.AI","cs.RO","cs.SY","eess.SY","stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2019-09-20T19:52:41Z","title_canon_sha256":"06bbf1c4611d28440211b7bbe2af7c30302d398a1e2ca32668c422fef9e9b5ef"},"schema_version":"1.0","source":{"id":"1909.09705","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1909.09705","created_at":"2026-07-05T00:06:13Z"},{"alias_kind":"arxiv_version","alias_value":"1909.09705v1","created_at":"2026-07-05T00:06:13Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1909.09705","created_at":"2026-07-05T00:06:13Z"},{"alias_kind":"pith_short_12","alias_value":"4JZAAIW5LNS7","created_at":"2026-07-05T00:06:13Z"},{"alias_kind":"pith_short_16","alias_value":"4JZAAIW5LNS7CVDR","created_at":"2026-07-05T00:06:13Z"},{"alias_kind":"pith_short_8","alias_value":"4JZAAIW5","created_at":"2026-07-05T00:06:13Z"}],"graph_snapshots":[{"event_id":"sha256:20672a0f79c8b17d9d72840b48eba4a487debf0e2d9775482270c91cfb89c07c","target":"graph","created_at":"2026-07-05T00:06:13Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/1909.09705/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We propose a planning and perception mechanism for a robot (agent), that can only observe the underlying environment partially, in order to solve an image classification problem. A three-layer architecture is suggested that consists of a meta-layer that decides the intermediate goals, an action-layer that selects local actions as the agent navigates towards a goal, and a classification-layer that evaluates the reward and makes a prediction. We design and implement these layers using deep reinforcement learning. A generalized policy gradient algorithm is utilized to learn the parameters of thes","authors_text":"Guangyi Liu, H\\'ector Mu\\~noz-Avila, Hossein K. Mousavi, Martin Tak\\'a\\v{c}, Nader Motee, Weihang Yuan","cross_cats":["cs.AI","cs.RO","cs.SY","eess.SY","stat.ML"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2019-09-20T19:52:41Z","title":"A Layered Architecture for Active Perception: Image Classification using Deep Reinforcement Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1909.09705","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:53e9c7a199ec10a987e32e086543e5ad010ba51839e3c023cf750b79debb7457","target":"record","created_at":"2026-07-05T00:06:13Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"a61d6feac87f8846573097a153beec41c7fb211d389d43c4a03ca2340666f03a","cross_cats_sorted":["cs.AI","cs.RO","cs.SY","eess.SY","stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2019-09-20T19:52:41Z","title_canon_sha256":"06bbf1c4611d28440211b7bbe2af7c30302d398a1e2ca32668c422fef9e9b5ef"},"schema_version":"1.0","source":{"id":"1909.09705","kind":"arxiv","version":1}},"canonical_sha256":"e2720022dd5b65f15471df251954ef71f26ecba7fe822b79e217501ae93d1328","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"e2720022dd5b65f15471df251954ef71f26ecba7fe822b79e217501ae93d1328","first_computed_at":"2026-07-05T00:06:13.564975Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T00:06:13.564975Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"ayD+Y19TPWhwMgZPZ6u6tTFsqMDpJCEUDg0yep3nc9vibIEU4zsDYd+BXTwy7g7JQZGx2npUrxGJUgq5u7iDDw==","signature_status":"signed_v1","signed_at":"2026-07-05T00:06:13.565401Z","signed_message":"canonical_sha256_bytes"},"source_id":"1909.09705","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:53e9c7a199ec10a987e32e086543e5ad010ba51839e3c023cf750b79debb7457","sha256:20672a0f79c8b17d9d72840b48eba4a487debf0e2d9775482270c91cfb89c07c"],"state_sha256":"d728e94e534fc1a9fd854d8c827321454cca3305465ec2932939cca2f33b56d4"}