{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:FLTE37IK5ODT7IKM5HOGNTD5LQ","short_pith_number":"pith:FLTE37IK","schema_version":"1.0","canonical_sha256":"2ae64dfd0aeb873fa14ce9dc66cc7d5c0dee1357c637bac06de2c10cea06d55e","source":{"kind":"arxiv","id":"2301.10034","version":3},"attestation_state":"computed","paper":{"title":"Open-World Multi-Task Control Through Goal-Aware Representation Learning and Adaptive Horizon Prediction","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Anji Liu, Shaofei Cai, Xiaojian Ma, Yitao Liang, Zihao Wang","submitted_at":"2023-01-21T08:15:38Z","abstract_excerpt":"We study the problem of learning goal-conditioned policies in Minecraft, a popular, widely accessible yet challenging open-ended environment for developing human-level multi-task agents. We first identify two main challenges of learning such policies: 1) the indistinguishability of tasks from the state distribution, due to the vast scene diversity, and 2) the non-stationary nature of environment dynamics caused by partial observability. To tackle the first challenge, we propose Goal-Sensitive Backbone (GSB) for the policy to encourage the emergence of goal-relevant visual state representations"},"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":"2301.10034","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2023-01-21T08:15:38Z","cross_cats_sorted":[],"title_canon_sha256":"2efb68cd4fefbd85d55c57dfd09fb7c95d902b2c403abae46cd1d948f2e4f573","abstract_canon_sha256":"8d477c0e3105e1e3d04f57dafebb33a3c0376a4284b4454add2be82bb4f798f5"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:00:20.825876Z","signature_b64":"nONLpRVs7Y49AnWNt30pqZR/RhxhHP0XNC7w7AR/fr6jIAQMwnLCs72ZxEe9PNcbM+48C8sqVFOicP7oo81oCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2ae64dfd0aeb873fa14ce9dc66cc7d5c0dee1357c637bac06de2c10cea06d55e","last_reissued_at":"2026-07-05T07:00:20.825417Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:00:20.825417Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Open-World Multi-Task Control Through Goal-Aware Representation Learning and Adaptive Horizon Prediction","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Anji Liu, Shaofei Cai, Xiaojian Ma, Yitao Liang, Zihao Wang","submitted_at":"2023-01-21T08:15:38Z","abstract_excerpt":"We study the problem of learning goal-conditioned policies in Minecraft, a popular, widely accessible yet challenging open-ended environment for developing human-level multi-task agents. We first identify two main challenges of learning such policies: 1) the indistinguishability of tasks from the state distribution, due to the vast scene diversity, and 2) the non-stationary nature of environment dynamics caused by partial observability. To tackle the first challenge, we propose Goal-Sensitive Backbone (GSB) for the policy to encourage the emergence of goal-relevant visual state representations"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2301.10034","kind":"arxiv","version":3},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2301.10034/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2301.10034","created_at":"2026-07-05T07:00:20.825483+00:00"},{"alias_kind":"arxiv_version","alias_value":"2301.10034v3","created_at":"2026-07-05T07:00:20.825483+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2301.10034","created_at":"2026-07-05T07:00:20.825483+00:00"},{"alias_kind":"pith_short_12","alias_value":"FLTE37IK5ODT","created_at":"2026-07-05T07:00:20.825483+00:00"},{"alias_kind":"pith_short_16","alias_value":"FLTE37IK5ODT7IKM","created_at":"2026-07-05T07:00:20.825483+00:00"},{"alias_kind":"pith_short_8","alias_value":"FLTE37IK","created_at":"2026-07-05T07:00:20.825483+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":3,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2302.01560","citing_title":"Describe, Explain, Plan and Select: Interactive Planning with Large Language Models Enables Open-World Multi-Task Agents","ref_index":6,"is_internal_anchor":false},{"citing_arxiv_id":"2305.17144","citing_title":"Ghost in the Minecraft: Generally Capable Agents for Open-World Environments via Large Language Models with Text-based Knowledge and Memory","ref_index":3,"is_internal_anchor":false},{"citing_arxiv_id":"2305.16291","citing_title":"Voyager: An Open-Ended Embodied Agent with Large Language Models","ref_index":54,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/FLTE37IK5ODT7IKM5HOGNTD5LQ","json":"https://pith.science/pith/FLTE37IK5ODT7IKM5HOGNTD5LQ.json","graph_json":"https://pith.science/api/pith-number/FLTE37IK5ODT7IKM5HOGNTD5LQ/graph.json","events_json":"https://pith.science/api/pith-number/FLTE37IK5ODT7IKM5HOGNTD5LQ/events.json","paper":"https://pith.science/paper/FLTE37IK"},"agent_actions":{"view_html":"https://pith.science/pith/FLTE37IK5ODT7IKM5HOGNTD5LQ","download_json":"https://pith.science/pith/FLTE37IK5ODT7IKM5HOGNTD5LQ.json","view_paper":"https://pith.science/paper/FLTE37IK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2301.10034&json=true","fetch_graph":"https://pith.science/api/pith-number/FLTE37IK5ODT7IKM5HOGNTD5LQ/graph.json","fetch_events":"https://pith.science/api/pith-number/FLTE37IK5ODT7IKM5HOGNTD5LQ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FLTE37IK5ODT7IKM5HOGNTD5LQ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FLTE37IK5ODT7IKM5HOGNTD5LQ/action/storage_attestation","attest_author":"https://pith.science/pith/FLTE37IK5ODT7IKM5HOGNTD5LQ/action/author_attestation","sign_citation":"https://pith.science/pith/FLTE37IK5ODT7IKM5HOGNTD5LQ/action/citation_signature","submit_replication":"https://pith.science/pith/FLTE37IK5ODT7IKM5HOGNTD5LQ/action/replication_record"}},"created_at":"2026-07-05T07:00:20.825483+00:00","updated_at":"2026-07-05T07:00:20.825483+00:00"}