{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2015:I773SERQVGZ5BHAKIM6MNMFK6Z","short_pith_number":"pith:I773SERQ","canonical_record":{"source":{"id":"1505.00322","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2015-05-02T08:29:09Z","cross_cats_sorted":["cs.RO"],"title_canon_sha256":"98c1b48693c5970e6a1c488448e261a70aba5208f9664c26fd2198e98dfa2ae3","abstract_canon_sha256":"1c3a2e18376af166c2e7c36ceae2e3aa2248e3c983a5bc4a999503b7c6c4cb55"},"schema_version":"1.0"},"canonical_sha256":"47ffb91230a9b3d09c0a433cc6b0aaf67b959dbe1dfea5103163f867efa2f1e8","source":{"kind":"arxiv","id":"1505.00322","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1505.00322","created_at":"2026-05-18T01:58:26Z"},{"alias_kind":"arxiv_version","alias_value":"1505.00322v2","created_at":"2026-05-18T01:58:26Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1505.00322","created_at":"2026-05-18T01:58:26Z"},{"alias_kind":"pith_short_12","alias_value":"I773SERQVGZ5","created_at":"2026-05-18T12:29:25Z"},{"alias_kind":"pith_short_16","alias_value":"I773SERQVGZ5BHAK","created_at":"2026-05-18T12:29:25Z"},{"alias_kind":"pith_short_8","alias_value":"I773SERQ","created_at":"2026-05-18T12:29:25Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2015:I773SERQVGZ5BHAKIM6MNMFK6Z","target":"record","payload":{"canonical_record":{"source":{"id":"1505.00322","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2015-05-02T08:29:09Z","cross_cats_sorted":["cs.RO"],"title_canon_sha256":"98c1b48693c5970e6a1c488448e261a70aba5208f9664c26fd2198e98dfa2ae3","abstract_canon_sha256":"1c3a2e18376af166c2e7c36ceae2e3aa2248e3c983a5bc4a999503b7c6c4cb55"},"schema_version":"1.0"},"canonical_sha256":"47ffb91230a9b3d09c0a433cc6b0aaf67b959dbe1dfea5103163f867efa2f1e8","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:58:26.679961Z","signature_b64":"qeJBTTqqkMgB0W8Celdx3mp3aaBY5OLUvePAWQ1lFh8JQmBH2tDGbWC+3N4SCeGypqNsctv56/aY11n/xQbEAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"47ffb91230a9b3d09c0a433cc6b0aaf67b959dbe1dfea5103163f867efa2f1e8","last_reissued_at":"2026-05-18T01:58:26.679377Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:58:26.679377Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1505.00322","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T01:58:26Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"FHGP/NBJbQXLyguTYn1a1EvH67NtMDJl2Zgh2yaW8sQXfazusr8qz9ZaydqZ60MWl3McxRiY8RL8opXYPDsWAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-05T14:03:28.117834Z"},"content_sha256":"30b21fe0ac4bd3896f12bdda9a6b52648e241347f8edee5c399e90b41611c65a","schema_version":"1.0","event_id":"sha256:30b21fe0ac4bd3896f12bdda9a6b52648e241347f8edee5c399e90b41611c65a"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2015:I773SERQVGZ5BHAKIM6MNMFK6Z","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Using PCA to Efficiently Represent State Spaces","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.RO"],"primary_cat":"cs.LG","authors_text":"Matthew Taylor, Tim Brys, William Curran, William Smart","submitted_at":"2015-05-02T08:29:09Z","abstract_excerpt":"Reinforcement learning algorithms need to deal with the exponential growth of states and actions when exploring optimal control in high-dimensional spaces. This is known as the curse of dimensionality. By projecting the agent's state onto a low-dimensional manifold, we can represent the state space in a smaller and more efficient representation. By using this representation during learning, the agent can converge to a good policy much faster. We test this approach in the Mario Benchmarking Domain. When using dimensionality reduction in Mario, learning converges much faster to a good policy. Bu"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1505.00322","kind":"arxiv","version":2},"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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T01:58:26Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"tBqZWrtM4O9NPYLt0I9+G/NBC4lLPZFEF+3cccVA59eabBo5G0bJmipZrEj4nRr+BkQkq2xsUb1W6LypNcLECQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-05T14:03:28.118177Z"},"content_sha256":"a0c1167f66fe724aa940efbd38f56393668189ec14cce85cda415e65d17671e0","schema_version":"1.0","event_id":"sha256:a0c1167f66fe724aa940efbd38f56393668189ec14cce85cda415e65d17671e0"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/I773SERQVGZ5BHAKIM6MNMFK6Z/bundle.json","state_url":"https://pith.science/pith/I773SERQVGZ5BHAKIM6MNMFK6Z/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/I773SERQVGZ5BHAKIM6MNMFK6Z/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-06-05T14:03:28Z","links":{"resolver":"https://pith.science/pith/I773SERQVGZ5BHAKIM6MNMFK6Z","bundle":"https://pith.science/pith/I773SERQVGZ5BHAKIM6MNMFK6Z/bundle.json","state":"https://pith.science/pith/I773SERQVGZ5BHAKIM6MNMFK6Z/state.json","well_known_bundle":"https://pith.science/.well-known/pith/I773SERQVGZ5BHAKIM6MNMFK6Z/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2015:I773SERQVGZ5BHAKIM6MNMFK6Z","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":"1c3a2e18376af166c2e7c36ceae2e3aa2248e3c983a5bc4a999503b7c6c4cb55","cross_cats_sorted":["cs.RO"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2015-05-02T08:29:09Z","title_canon_sha256":"98c1b48693c5970e6a1c488448e261a70aba5208f9664c26fd2198e98dfa2ae3"},"schema_version":"1.0","source":{"id":"1505.00322","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1505.00322","created_at":"2026-05-18T01:58:26Z"},{"alias_kind":"arxiv_version","alias_value":"1505.00322v2","created_at":"2026-05-18T01:58:26Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1505.00322","created_at":"2026-05-18T01:58:26Z"},{"alias_kind":"pith_short_12","alias_value":"I773SERQVGZ5","created_at":"2026-05-18T12:29:25Z"},{"alias_kind":"pith_short_16","alias_value":"I773SERQVGZ5BHAK","created_at":"2026-05-18T12:29:25Z"},{"alias_kind":"pith_short_8","alias_value":"I773SERQ","created_at":"2026-05-18T12:29:25Z"}],"graph_snapshots":[{"event_id":"sha256:a0c1167f66fe724aa940efbd38f56393668189ec14cce85cda415e65d17671e0","target":"graph","created_at":"2026-05-18T01:58:26Z","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"},"paper":{"abstract_excerpt":"Reinforcement learning algorithms need to deal with the exponential growth of states and actions when exploring optimal control in high-dimensional spaces. This is known as the curse of dimensionality. By projecting the agent's state onto a low-dimensional manifold, we can represent the state space in a smaller and more efficient representation. By using this representation during learning, the agent can converge to a good policy much faster. We test this approach in the Mario Benchmarking Domain. When using dimensionality reduction in Mario, learning converges much faster to a good policy. Bu","authors_text":"Matthew Taylor, Tim Brys, William Curran, William Smart","cross_cats":["cs.RO"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2015-05-02T08:29:09Z","title":"Using PCA to Efficiently Represent State Spaces"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1505.00322","kind":"arxiv","version":2},"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":"sha256:30b21fe0ac4bd3896f12bdda9a6b52648e241347f8edee5c399e90b41611c65a","target":"record","created_at":"2026-05-18T01:58:26Z","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":"1c3a2e18376af166c2e7c36ceae2e3aa2248e3c983a5bc4a999503b7c6c4cb55","cross_cats_sorted":["cs.RO"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2015-05-02T08:29:09Z","title_canon_sha256":"98c1b48693c5970e6a1c488448e261a70aba5208f9664c26fd2198e98dfa2ae3"},"schema_version":"1.0","source":{"id":"1505.00322","kind":"arxiv","version":2}},"canonical_sha256":"47ffb91230a9b3d09c0a433cc6b0aaf67b959dbe1dfea5103163f867efa2f1e8","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"47ffb91230a9b3d09c0a433cc6b0aaf67b959dbe1dfea5103163f867efa2f1e8","first_computed_at":"2026-05-18T01:58:26.679377Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:58:26.679377Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"qeJBTTqqkMgB0W8Celdx3mp3aaBY5OLUvePAWQ1lFh8JQmBH2tDGbWC+3N4SCeGypqNsctv56/aY11n/xQbEAQ==","signature_status":"signed_v1","signed_at":"2026-05-18T01:58:26.679961Z","signed_message":"canonical_sha256_bytes"},"source_id":"1505.00322","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:30b21fe0ac4bd3896f12bdda9a6b52648e241347f8edee5c399e90b41611c65a","sha256:a0c1167f66fe724aa940efbd38f56393668189ec14cce85cda415e65d17671e0"],"state_sha256":"ea71a4beb976df07b0b2ae2fff6814bccecd3e91ff226f053031d1930b3ffd44"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"m/nBb2VWVHEOEG6orciYzf0Qh9nyCrYxAoYjKwyAXDkdT+mzuGgW44IWCNZYNrRKs5PA3Uj58RAvs0j6AGDJBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-05T14:03:28.120183Z","bundle_sha256":"99279c7fb289e3fdfbe3c33ab81d929aa6332da2d56b4063708f558170fd7acd"}}