{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:Z4WA3TYRPOVBM62LRHZMN32HN6","short_pith_number":"pith:Z4WA3TYR","schema_version":"1.0","canonical_sha256":"cf2c0dcf117baa167b4b89f2c6ef476f9cdbda1d8c09c5143ac344e4127ad9fc","source":{"kind":"arxiv","id":"1609.05143","version":1},"attestation_state":"computed","paper":{"title":"Target-driven Visual Navigation in Indoor Scenes using Deep Reinforcement Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Abhinav Gupta, Ali Farhadi, Eric Kolve, Joseph J. Lim, Li Fei-Fei, Roozbeh Mottaghi, Yuke Zhu","submitted_at":"2016-09-16T17:16:49Z","abstract_excerpt":"Two less addressed issues of deep reinforcement learning are (1) lack of generalization capability to new target goals, and (2) data inefficiency i.e., the model requires several (and often costly) episodes of trial and error to converge, which makes it impractical to be applied to real-world scenarios. In this paper, we address these two issues and apply our model to the task of target-driven visual navigation. To address the first issue, we propose an actor-critic model whose policy is a function of the goal as well as the current state, which allows to better generalize. To address the seco"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1609.05143","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-09-16T17:16:49Z","cross_cats_sorted":[],"title_canon_sha256":"9189e45d9b775a86deb8b9d4fa792c33a56a0da14b5be224eb5eac7a369e5ef9","abstract_canon_sha256":"fe368967d2dfe020ee22fade2b7981c631309f0f840fa25ff6c20e2da5fd00d3"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:04:31.366675Z","signature_b64":"9IUaYSUOiQFmHDL7uGPPWbzG4mog6W/PHH5BCEIgSheK73H55rCn34YILLU69khq+q3pd4HHeqqeqCFw+5D8Ag==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cf2c0dcf117baa167b4b89f2c6ef476f9cdbda1d8c09c5143ac344e4127ad9fc","last_reissued_at":"2026-05-18T01:04:31.365943Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:04:31.365943Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Target-driven Visual Navigation in Indoor Scenes using Deep Reinforcement Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Abhinav Gupta, Ali Farhadi, Eric Kolve, Joseph J. Lim, Li Fei-Fei, Roozbeh Mottaghi, Yuke Zhu","submitted_at":"2016-09-16T17:16:49Z","abstract_excerpt":"Two less addressed issues of deep reinforcement learning are (1) lack of generalization capability to new target goals, and (2) data inefficiency i.e., the model requires several (and often costly) episodes of trial and error to converge, which makes it impractical to be applied to real-world scenarios. In this paper, we address these two issues and apply our model to the task of target-driven visual navigation. To address the first issue, we propose an actor-critic model whose policy is a function of the goal as well as the current state, which allows to better generalize. To address the seco"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1609.05143","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"1609.05143","created_at":"2026-05-18T01:04:31.366046+00:00"},{"alias_kind":"arxiv_version","alias_value":"1609.05143v1","created_at":"2026-05-18T01:04:31.366046+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1609.05143","created_at":"2026-05-18T01:04:31.366046+00:00"},{"alias_kind":"pith_short_12","alias_value":"Z4WA3TYRPOVB","created_at":"2026-05-18T12:30:53.716459+00:00"},{"alias_kind":"pith_short_16","alias_value":"Z4WA3TYRPOVBM62L","created_at":"2026-05-18T12:30:53.716459+00:00"},{"alias_kind":"pith_short_8","alias_value":"Z4WA3TYR","created_at":"2026-05-18T12:30:53.716459+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/Z4WA3TYRPOVBM62LRHZMN32HN6","json":"https://pith.science/pith/Z4WA3TYRPOVBM62LRHZMN32HN6.json","graph_json":"https://pith.science/api/pith-number/Z4WA3TYRPOVBM62LRHZMN32HN6/graph.json","events_json":"https://pith.science/api/pith-number/Z4WA3TYRPOVBM62LRHZMN32HN6/events.json","paper":"https://pith.science/paper/Z4WA3TYR"},"agent_actions":{"view_html":"https://pith.science/pith/Z4WA3TYRPOVBM62LRHZMN32HN6","download_json":"https://pith.science/pith/Z4WA3TYRPOVBM62LRHZMN32HN6.json","view_paper":"https://pith.science/paper/Z4WA3TYR","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1609.05143&json=true","fetch_graph":"https://pith.science/api/pith-number/Z4WA3TYRPOVBM62LRHZMN32HN6/graph.json","fetch_events":"https://pith.science/api/pith-number/Z4WA3TYRPOVBM62LRHZMN32HN6/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/Z4WA3TYRPOVBM62LRHZMN32HN6/action/timestamp_anchor","attest_storage":"https://pith.science/pith/Z4WA3TYRPOVBM62LRHZMN32HN6/action/storage_attestation","attest_author":"https://pith.science/pith/Z4WA3TYRPOVBM62LRHZMN32HN6/action/author_attestation","sign_citation":"https://pith.science/pith/Z4WA3TYRPOVBM62LRHZMN32HN6/action/citation_signature","submit_replication":"https://pith.science/pith/Z4WA3TYRPOVBM62LRHZMN32HN6/action/replication_record"}},"created_at":"2026-05-18T01:04:31.366046+00:00","updated_at":"2026-05-18T01:04:31.366046+00:00"}