Paper Citation Record · LEDGER

From Crystallized Adaptivity to Fluid Adaptivity in Deep Reinforcement Learning -- Insights from Biological Systems on Adaptive Flexibility

As of 16 August 2026, this Paper Citation Record lists 28 of 28 outbound references and 0 inbound Pith citation observations for arXiv:1908.05348.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
1908.05348 v1

Coverage vector

measured 28 of 28 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-14T13:44:26.757733Z

measured 28 of 28 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

28 of 28 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 426509fc-2cf4-4415-b345-b4de74bda9e0 · outbound

This paper cites Reinforcement learning in artiﬁcial and biological systems,.

From Crystallized Adaptivity to Fluid Adaptivity in Deep Reinforcement Learning -- Insights from Biological Systems on Adaptive Flexibility Reinforcement learning in artiﬁcial and biological systems,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:44:27.030773Z

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Observation ad4ece95-e5fd-47fb-b1cf-a0f9511922de · outbound

This paper cites an unresolved cited work.

From Crystallized Adaptivity to Fluid Adaptivity in Deep Reinforcement Learning -- Insights from Biological Systems on Adaptive Flexibility Unresolved cited work

Reference 2

Resolution: unresolved; raw_fallback, observed 2026-08-14T13:44:27.020695Z

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Observation 3f1565c7-5a81-43a6-919f-bc131ce7f8c5 · outbound

This paper cites Mastering the game of Go with deep neural networks and tree search,.

From Crystallized Adaptivity to Fluid Adaptivity in Deep Reinforcement Learning -- Insights from Biological Systems on Adaptive Flexibility Mastering the game of Go with deep neural networks and tree search,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:44:27.011311Z

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Observation 1d86a1ba-2710-4b38-8756-7afb39cc8e0f · outbound

This paper cites Neuroscience-Inspired Artiﬁcial Intelligence.

From Crystallized Adaptivity to Fluid Adaptivity in Deep Reinforcement Learning -- Insights from Biological Systems on Adaptive Flexibility Neuroscience-Inspired Artiﬁcial Intelligence

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:44:27.001582Z

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Observation f874555a-0b91-4507-9f54-dc543fd54fd8 · outbound

This paper cites Robots that can adapt like animals.

From Crystallized Adaptivity to Fluid Adaptivity in Deep Reinforcement Learning -- Insights from Biological Systems on Adaptive Flexibility Robots that can adapt like animals

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:44:26.992247Z

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Observation 4e3d20db-8a63-4463-8d7b-cbd10bd09fb5 · outbound

This paper cites Cattell, Abilities: Their Structure, Growth, and Action.

From Crystallized Adaptivity to Fluid Adaptivity in Deep Reinforcement Learning -- Insights from Biological Systems on Adaptive Flexibility Cattell, Abilities: Their Structure, Growth, and Action

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:44:26.983199Z

Source-reported events for the cited work

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Observation d6c67f9a-40ac-4451-b502-c5cad2d601c3 · outbound

This paper cites Human-level control through deep reinforcement learning,.

From Crystallized Adaptivity to Fluid Adaptivity in Deep Reinforcement Learning -- Insights from Biological Systems on Adaptive Flexibility Human-level control through deep reinforcement learning,

Reference 7

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Observation b58e395a-4e0f-4ca8-a955-ffac72bd99c0 · outbound

This paper cites Continuous control with deep reinforcement learning.

From Crystallized Adaptivity to Fluid Adaptivity in Deep Reinforcement Learning -- Insights from Biological Systems on Adaptive Flexibility Continuous control with deep reinforcement learning

Reference 8

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Observation 0149d988-c17f-4e1e-a724-3471c7b1ab4b · outbound

This paper cites Simple genetic algorithms and the minimal, decep- tive problem,.

From Crystallized Adaptivity to Fluid Adaptivity in Deep Reinforcement Learning -- Insights from Biological Systems on Adaptive Flexibility Simple genetic algorithms and the minimal, decep- tive problem,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:44:26.969532Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 0aee3fc6-7af4-4b3b-aed0-0299d83180f3 · outbound

This paper cites Abandoning objectives: evolution through the search for novelty alone.

From Crystallized Adaptivity to Fluid Adaptivity in Deep Reinforcement Learning -- Insights from Biological Systems on Adaptive Flexibility Abandoning objectives: evolution through the search for novelty alone

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:44:26.961044Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 36639674-11fa-4877-8393-46916cfee8be · outbound

This paper cites Hier- archical deep reinforcement learning: Integrating temporal abstraction and intrinsic motivation,.

From Crystallized Adaptivity to Fluid Adaptivity in Deep Reinforcement Learning -- Insights from Biological Systems on Adaptive Flexibility Hier- archical deep reinforcement learning: Integrating temporal abstraction and intrinsic motivation,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:44:26.951928Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 49c0dbd5-959d-4f11-9d03-a982e3b7d663 · outbound

This paper cites Walknet, a bio-inspired controller for hexapod walking.

From Crystallized Adaptivity to Fluid Adaptivity in Deep Reinforcement Learning -- Insights from Biological Systems on Adaptive Flexibility Walknet, a bio-inspired controller for hexapod walking

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:44:26.942664Z

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Observation e8fcd750-b2be-4858-a2ac-abb75e0f18f3 · outbound

This paper cites Illuminating search spaces by mapping elites.

From Crystallized Adaptivity to Fluid Adaptivity in Deep Reinforcement Learning -- Insights from Biological Systems on Adaptive Flexibility Illuminating search spaces by mapping elites

Reference 13

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Observation 004dc0f8-5195-4ef8-bf72-ca813d3ee846 · outbound

This paper cites A hexapod walker using a heterarchical architecture for action selection.

From Crystallized Adaptivity to Fluid Adaptivity in Deep Reinforcement Learning -- Insights from Biological Systems on Adaptive Flexibility A hexapod walker using a heterarchical architecture for action selection

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:44:26.933298Z

Source-reported events for the cited work

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Observation d7602c82-9124-41f6-9f31-18cc5552b796 · outbound

This paper cites Human-level performance in first-person multiplayer games with population-based deep reinforcement learning.

From Crystallized Adaptivity to Fluid Adaptivity in Deep Reinforcement Learning -- Insights from Biological Systems on Adaptive Flexibility Human-level performance in first-person multiplayer games with population-based deep reinforcement learning

Reference 15

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Observation 170397f8-49f3-4cd4-836b-dff6cca2ba7b · outbound

This paper cites Deep Reinforcement Learning with Double Q-Learning,.

From Crystallized Adaptivity to Fluid Adaptivity in Deep Reinforcement Learning -- Insights from Biological Systems on Adaptive Flexibility Deep Reinforcement Learning with Double Q-Learning,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:44:26.924551Z

Source-reported events for the cited work

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Observation 9c23bd38-0bb3-4e7a-a594-563285510f9e · outbound

This paper cites Addressing Function Approximation Error in Actor-Critic Methods.

From Crystallized Adaptivity to Fluid Adaptivity in Deep Reinforcement Learning -- Insights from Biological Systems on Adaptive Flexibility Addressing Function Approximation Error in Actor-Critic Methods

Reference 17

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Observation da64b35c-0241-45e6-867f-8a04bac5fd05 · outbound

This paper cites Model-based reinforcement learning under concurrent schedules of reinforcement in rodents,.

From Crystallized Adaptivity to Fluid Adaptivity in Deep Reinforcement Learning -- Insights from Biological Systems on Adaptive Flexibility Model-based reinforcement learning under concurrent schedules of reinforcement in rodents,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:44:26.915194Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 3129bd56-2d5d-4464-aa2a-0a2dc219cfab · outbound

This paper cites Reversal Learning in Humans and Gerbils: Dynamic Control Network Facilitates Learning,.

From Crystallized Adaptivity to Fluid Adaptivity in Deep Reinforcement Learning -- Insights from Biological Systems on Adaptive Flexibility Reversal Learning in Humans and Gerbils: Dynamic Control Network Facilitates Learning,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:44:26.906648Z

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation ced43104-562c-457f-af9e-e282be11440a · outbound

This paper cites Selective increase of auditory cortico-striatal coherence during auditory-cued go/nogo discrimination learning,.

From Crystallized Adaptivity to Fluid Adaptivity in Deep Reinforcement Learning -- Insights from Biological Systems on Adaptive Flexibility Selective increase of auditory cortico-striatal coherence during auditory-cued go/nogo discrimination learning,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:44:26.896893Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 839e249a-a049-4e10-bde3-29762caefd59 · outbound

This paper cites The Predictron: End-to-End Learning and Planning,.

From Crystallized Adaptivity to Fluid Adaptivity in Deep Reinforcement Learning -- Insights from Biological Systems on Adaptive Flexibility The Predictron: End-to-End Learning and Planning,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:44:26.887321Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation f4690816-cb36-42c8-a1c5-58659b658104 · outbound

This paper cites Hexapod Walking: an expansion to Walknet dealing with leg amputations and force oscillations,.

From Crystallized Adaptivity to Fluid Adaptivity in Deep Reinforcement Learning -- Insights from Biological Systems on Adaptive Flexibility Hexapod Walking: an expansion to Walknet dealing with leg amputations and force oscillations,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:44:26.878221Z

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 0ea50043-63b3-440e-845f-f28ca5fcaf00 · outbound

This paper cites How Animals Move: An Integrative View,.

From Crystallized Adaptivity to Fluid Adaptivity in Deep Reinforcement Learning -- Insights from Biological Systems on Adaptive Flexibility How Animals Move: An Integrative View,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:44:26.868344Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation ddcaf97c-47e9-427f-be32-2fa3c4ec4d84 · outbound

This paper cites An Approach to Hierarchical Deep Reinforcement Learning for a Decentralized Walking Control Ar- chitecture,.

From Crystallized Adaptivity to Fluid Adaptivity in Deep Reinforcement Learning -- Insights from Biological Systems on Adaptive Flexibility An Approach to Hierarchical Deep Reinforcement Learning for a Decentralized Walking Control Ar- chitecture,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:44:26.857924Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation f5411932-2993-468d-800d-cc4bc05da506 · outbound

This paper cites Vinyals, I.

From Crystallized Adaptivity to Fluid Adaptivity in Deep Reinforcement Learning -- Insights from Biological Systems on Adaptive Flexibility Vinyals, I

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:44:26.845732Z

Source-reported events for the cited work

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Observation ae151664-073e-44db-8259-7ef0446511a3 · outbound

This paper cites Open-ended Learning in Symmetric Zero-sum Games.

From Crystallized Adaptivity to Fluid Adaptivity in Deep Reinforcement Learning -- Insights from Biological Systems on Adaptive Flexibility Open-ended Learning in Symmetric Zero-sum Games

Reference 26

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Observation 687ed6f6-0c7d-4e37-b705-b3087f4f3a8d · outbound

This paper cites The Value Function Polytope in Reinforcement Learning.

From Crystallized Adaptivity to Fluid Adaptivity in Deep Reinforcement Learning -- Insights from Biological Systems on Adaptive Flexibility The Value Function Polytope in Reinforcement Learning

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-14T13:44:26.757733Z

Source-reported events for the cited work

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Observation 99974729-643e-4c64-88f7-a5ddfe94a7f6 · outbound

This paper cites AlphaStar: An Evolutionary Computation Perspective.

From Crystallized Adaptivity to Fluid Adaptivity in Deep Reinforcement Learning -- Insights from Biological Systems on Adaptive Flexibility AlphaStar: An Evolutionary Computation Perspective

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-14T13:44:26.754659Z

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Pith citing papers

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