{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:PR66RFDOMCWTUHIKAHVG43U36G","short_pith_number":"pith:PR66RFDO","schema_version":"1.0","canonical_sha256":"7c7de8946e60ad3a1d0a01ea6e6e9bf1a524e21d86ad58c359d24b7b14af5687","source":{"kind":"arxiv","id":"2307.00994","version":2},"attestation_state":"computed","paper":{"title":"Environmental effects on emergent strategy in micro-scale multi-agent reinforcement learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG","cs.RO"],"primary_cat":"physics.bio-ph","authors_text":"Christian Holm, Christoph Lohrmann, Clemens Bechinger, David Zimmer, Samuel Tovey, Simon Koppenhoefer, Tobias Merkt, Veit-Lorenz Heuthe","submitted_at":"2023-07-03T13:18:25Z","abstract_excerpt":"Multi-Agent Reinforcement Learning (MARL) is a promising candidate for realizing efficient control of microscopic particles, of which micro-robots are a subset. However, the microscopic particles' environment presents unique challenges, such as Brownian motion at sufficiently small length-scales. In this work, we explore the role of temperature in the emergence and efficacy of strategies in MARL systems using particle-based Langevin molecular dynamics simulations as a realistic representation of micro-scale environments. To this end, we perform experiments on two different multi-agent tasks in"},"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":"2307.00994","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"physics.bio-ph","submitted_at":"2023-07-03T13:18:25Z","cross_cats_sorted":["cs.LG","cs.RO"],"title_canon_sha256":"de7abc4e07755d2952711ffb026436ae9048e6774b68fad6a558fcb579d372e0","abstract_canon_sha256":"6f57aa90dce7945a4858805ad7d5941bb14aed104888d33f93886a1cfffafb27"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:53:08.491387Z","signature_b64":"3BrL52K5IpuKNSIURntHH+XIFOBgGorSpSrLITch/EXjp8RJSxS3qTGAD225r/EieKYTxjbNQabQdMgdMx5lBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7c7de8946e60ad3a1d0a01ea6e6e9bf1a524e21d86ad58c359d24b7b14af5687","last_reissued_at":"2026-07-05T06:53:08.490953Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:53:08.490953Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Environmental effects on emergent strategy in micro-scale multi-agent reinforcement learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG","cs.RO"],"primary_cat":"physics.bio-ph","authors_text":"Christian Holm, Christoph Lohrmann, Clemens Bechinger, David Zimmer, Samuel Tovey, Simon Koppenhoefer, Tobias Merkt, Veit-Lorenz Heuthe","submitted_at":"2023-07-03T13:18:25Z","abstract_excerpt":"Multi-Agent Reinforcement Learning (MARL) is a promising candidate for realizing efficient control of microscopic particles, of which micro-robots are a subset. However, the microscopic particles' environment presents unique challenges, such as Brownian motion at sufficiently small length-scales. In this work, we explore the role of temperature in the emergence and efficacy of strategies in MARL systems using particle-based Langevin molecular dynamics simulations as a realistic representation of micro-scale environments. To this end, we perform experiments on two different multi-agent tasks in"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2307.00994","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2307.00994/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":"2307.00994","created_at":"2026-07-05T06:53:08.491008+00:00"},{"alias_kind":"arxiv_version","alias_value":"2307.00994v2","created_at":"2026-07-05T06:53:08.491008+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2307.00994","created_at":"2026-07-05T06:53:08.491008+00:00"},{"alias_kind":"pith_short_12","alias_value":"PR66RFDOMCWT","created_at":"2026-07-05T06:53:08.491008+00:00"},{"alias_kind":"pith_short_16","alias_value":"PR66RFDOMCWTUHIK","created_at":"2026-07-05T06:53:08.491008+00:00"},{"alias_kind":"pith_short_8","alias_value":"PR66RFDO","created_at":"2026-07-05T06:53:08.491008+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2501.08632","citing_title":"Towards Intelligent Active Particles","ref_index":67,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/PR66RFDOMCWTUHIKAHVG43U36G","json":"https://pith.science/pith/PR66RFDOMCWTUHIKAHVG43U36G.json","graph_json":"https://pith.science/api/pith-number/PR66RFDOMCWTUHIKAHVG43U36G/graph.json","events_json":"https://pith.science/api/pith-number/PR66RFDOMCWTUHIKAHVG43U36G/events.json","paper":"https://pith.science/paper/PR66RFDO"},"agent_actions":{"view_html":"https://pith.science/pith/PR66RFDOMCWTUHIKAHVG43U36G","download_json":"https://pith.science/pith/PR66RFDOMCWTUHIKAHVG43U36G.json","view_paper":"https://pith.science/paper/PR66RFDO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2307.00994&json=true","fetch_graph":"https://pith.science/api/pith-number/PR66RFDOMCWTUHIKAHVG43U36G/graph.json","fetch_events":"https://pith.science/api/pith-number/PR66RFDOMCWTUHIKAHVG43U36G/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PR66RFDOMCWTUHIKAHVG43U36G/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PR66RFDOMCWTUHIKAHVG43U36G/action/storage_attestation","attest_author":"https://pith.science/pith/PR66RFDOMCWTUHIKAHVG43U36G/action/author_attestation","sign_citation":"https://pith.science/pith/PR66RFDOMCWTUHIKAHVG43U36G/action/citation_signature","submit_replication":"https://pith.science/pith/PR66RFDOMCWTUHIKAHVG43U36G/action/replication_record"}},"created_at":"2026-07-05T06:53:08.491008+00:00","updated_at":"2026-07-05T06:53:08.491008+00:00"}