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Paper Citation Record · LEDGER

Practical Risk Measures in Reinforcement Learning

As of 22 August 2026, this Paper Citation Record lists 44 of 44 outbound references and 0 inbound Pith citation observations for arXiv:1908.08379.

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

pith.paper-citation-record.v1
1908.08379 v1

Coverage vector

measured 44 of 44 reference resolution

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measured 44 of 44 standing notices

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measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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Source: cited_works

Reference resolution

44 of 44 outbound references displayed

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External citation measurements

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Outbound references

Observation c0622a28-0221-4059-bcb0-06077b7047cb · outbound

This paper cites Constrained Policy Optimization.

Practical Risk Measures in Reinforcement Learning Constrained Policy Optimization

Reference 1

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Practical Risk Measures in Reinforcement Learning Unresolved cited work

Reference 9

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This paper cites A comprehensive survey on safe reinforce- ment learning.

Practical Risk Measures in Reinforcement Learning A comprehensive survey on safe reinforce- ment learning

Reference 12

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This paper cites Risk-sensitive reinforcement learning applied to control under constraints.

Practical Risk Measures in Reinforcement Learning Risk-sensitive reinforcement learning applied to control under constraints

Reference 13

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This paper cites Likelihood ratio gradient es- timation for stochastic systems.

Practical Risk Measures in Reinforcement Learning Likelihood ratio gradient es- timation for stochastic systems

Reference 14

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This paper cites Risk-sensitive markov decision processes.

Practical Risk Measures in Reinforcement Learning Risk-sensitive markov decision processes

Reference 16

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This paper cites Func- tional value iteration for decision-theoretic planning with general utility functions.

Practical Risk Measures in Reinforcement Learning Func- tional value iteration for decision-theoretic planning with general utility functions

Reference 22

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This paper cites Mean-Variance Optimization in Markov Decision Processes.

Practical Risk Measures in Reinforcement Learning Mean-Variance Optimization in Markov Decision Processes

Reference 25

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Observation 864485fc-1928-468d-a3e3-b0f4becf540b · outbound

This paper cites Safe Exploration in Markov Decision Processes.

Practical Risk Measures in Reinforcement Learning Safe Exploration in Markov Decision Processes

Reference 28

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This paper cites Parametric Return Density Estimation for Reinforcement Learning.

Practical Risk Measures in Reinforcement Learning Parametric Return Density Estimation for Reinforcement Learning

Reference 29

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This paper cites Policy invariance under reward transformations: Theory and application to reward shaping.

Practical Risk Measures in Reinforcement Learning Policy invariance under reward transformations: Theory and application to reward shaping

Reference 30

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This paper cites Markov chains.

Practical Risk Measures in Reinforcement Learning Markov chains

Reference 31

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This paper cites Evaluation: from preci- sion, recall and f-measure to roc, informedness, markedness and correlation.

Practical Risk Measures in Reinforcement Learning Evaluation: from preci- sion, recall and f-measure to roc, informedness, markedness and correlation

Reference 33

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This paper cites Actor-critic algorithms for risk- sensitive mdps.

Practical Risk Measures in Reinforcement Learning Actor-critic algorithms for risk- sensitive mdps

Reference 34

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Practical Risk Measures in Reinforcement Learning Markov decision pro- cesses

Reference 35

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This paper cites Td algorithm for the variance of return and mean-variance reinforcement learning.

Practical Risk Measures in Reinforcement Learning Td algorithm for the variance of return and mean-variance reinforcement learning

Reference 37

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This paper cites Lectures on stochastic program- ming: modeling and theory.

Practical Risk Measures in Reinforcement Learning Lectures on stochastic program- ming: modeling and theory

Reference 38

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Practical Risk Measures in Reinforcement Learning Temporal difference methods for the variance of the reward to go

Reference 43

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Observation 1d70e6be-1833-424a-8977-010079b7e4bf · outbound

This paper cites The gambler’s ruin ap- proach to business risk.

Practical Risk Measures in Reinforcement Learning The gambler’s ruin ap- proach to business risk

Reference 44

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Practical Risk Measures in Reinforcement Learning Unresolved cited work

Reference 1186

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Practical Risk Measures in Reinforcement Learning Dynamic probabilistic systems: Markov models, volume

Reference 1972

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This paper cites Percentile performance criteria for limit- ing average markov decision processes.

Practical Risk Measures in Reinforcement Learning Percentile performance criteria for limit- ing average markov decision processes

Reference 1974

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Observation 47e56ef4-44d9-4e50-8ad3-296d7d0ff9cc · outbound

This paper cites Reinforcement learning: An introduction.

Practical Risk Measures in Reinforcement Learning Reinforcement learning: An introduction

Reference 1982

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Observation e5516cde-bf17-4998-86c7-643b5d37f6c1 · outbound

This paper cites Policy gradients with variance related risk criteria.

Practical Risk Measures in Reinforcement Learning Policy gradients with variance related risk criteria

Reference 1988

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation 24f94fc6-a7ea-4083-a22b-da5919f98226 · outbound

This paper cites Deep learning, vol- ume.

Practical Risk Measures in Reinforcement Learning Deep learning, vol- ume

Reference 1990

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Observation fc743c7c-1e30-4516-a20a-4fe2e007a427 · outbound

This paper cites Optimization of conditional value-at-risk.

Practical Risk Measures in Reinforcement Learning Optimization of conditional value-at-risk

Reference 1994

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Practical Risk Measures in Reinforcement Learning Monte Carlo: concepts, algorithms, and applications

Reference 1995

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation fe836923-52ea-4ca8-991f-ffbd96e51f10 · outbound

This paper cites Safe policy iteration.

Practical Risk Measures in Reinforcement Learning Safe policy iteration

Reference 1998

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation 3e7b3fcc-7d6c-457f-9554-80bac6e4a827 · outbound

This paper cites Safe policy search for lifelong reinforce- ment learning with sublinear regret.

Practical Risk Measures in Reinforcement Learning Safe policy search for lifelong reinforce- ment learning with sublinear regret

Reference 1999

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation 9659f2d8-4bff-4b40-9f93-49a21b1bd4c9 · outbound

This paper cites Stochastic approximation and recursive algorithms and applications, volume.

Practical Risk Measures in Reinforcement Learning Stochastic approximation and recursive algorithms and applications, volume

Reference 2000

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation 99ac038d-8fbf-4f59-85c3-b88876d70d80 · outbound

This paper cites Dy- namic programming and optimal control, volume.

Practical Risk Measures in Reinforcement Learning Dy- namic programming and optimal control, volume

Reference 2001

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Practical Risk Measures in Reinforcement Learning Human-level control through deep reinforcement learning

Reference 2002

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Practical Risk Measures in Reinforcement Learning Deep learning

Reference 2003

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation 82567b9a-80e6-4a58-ae6f-01dd20053b60 · outbound

This paper cites Model predictive control.

Practical Risk Measures in Reinforcement Learning Model predictive control

Reference 2005

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation c4994c29-6bdb-441c-a921-5c0017937f81 · outbound

This paper cites Existence and Finiteness Conditions for Risk-Sensitive Planning: Results and Conjectures.

Practical Risk Measures in Reinforcement Learning Existence and Finiteness Conditions for Risk-Sensitive Planning: Results and Conjectures

Reference 2006

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation 98a3397f-b14f-427d-b44e-c62e1e69eb7c · outbound

This paper cites The variance of discounted markov decision processes.

Practical Risk Measures in Reinforcement Learning The variance of discounted markov decision processes

Reference 2009

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation d88b01fb-298f-40c1-a244-7d19c6fc2aef · outbound

This paper cites Risk-sensitive reinforcement learning.

Practical Risk Measures in Reinforcement Learning Risk-sensitive reinforcement learning

Reference 2011

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-14T11:47:00.340096Z digest=sha256:4f70572bdc12bb080cb5802db820d15bf3b046d68ca5d8f83503538628dfe8fb

Observation a8c73675-244b-4b88-bef3-af6ef33686bd · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Practical Risk Measures in Reinforcement Learning Adam: A Method for Stochastic Optimization

Reference 2012

Resolution
unresolved
no resolver link, observed 2026-08-14T11:47:00.296273Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-14T11:47:00.296273Z digest=sha256:af6cecb3b217b5091878dc29ad7f73a999c310d80bad66d1862c9c013bf648dc

Observation f8cda374-623a-4141-b2c3-32946a77b97b · outbound

This paper cites Risk-constrained reinforcement learning with percentile risk criteria.

Practical Risk Measures in Reinforcement Learning Risk-constrained reinforcement learning with percentile risk criteria

Reference 2013

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T11:47:01.243517Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-14T11:47:00.241793Z digest=sha256:8e88778547589b471d3565456a062590a7020815ee070987efd2d4bd078aeae4

Observation f758d6f8-8c2f-47db-b1aa-d75bdddf43f3 · outbound

This paper cites Actor-critic algorithms.

Practical Risk Measures in Reinforcement Learning Actor-critic algorithms

Reference 2014

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T11:47:01.072180Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-14T11:47:00.302243Z digest=sha256:f0812646ace8001f89a90052777f74f741109cf7d103ba22571cf2b10b0d0fc8

Observation 1b2a7ee7-e7c6-4da9-b510-610e36ec7c36 · outbound

This paper cites Concrete Problems in AI Safety.

Practical Risk Measures in Reinforcement Learning Concrete Problems in AI Safety

Reference 2015

Resolution
unresolved
no resolver link, observed 2026-08-14T11:47:00.216670Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-14T11:47:00.216670Z digest=sha256:741bda6009109776c7beafd6c86042e6c247026020da59c747363485fd67763f

Observation 667f2c22-fb2c-40f6-b2e6-5872d5282a4e · outbound

This paper cites Infinite-horizon policy-gradient estimation.

Practical Risk Measures in Reinforcement Learning Infinite-horizon policy-gradient estimation

Reference 2016

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T11:47:01.294961Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-14T11:47:00.223403Z digest=sha256:69c065f5f033a77335d00d519b667115ab5367ec675be6374b72c9037b7d3ecd

Observation c953d1a8-b7ee-4404-bf53-d5e7271a4d22 · outbound

This paper cites Constrained Markov decision processes, volume.

Practical Risk Measures in Reinforcement Learning Constrained Markov decision processes, volume

Reference 2017

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T11:47:01.328689Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-14T11:47:00.205811Z digest=sha256:3a778c6e99467c93af986de9f2038a5313418c347b10ad35dd4fd177e9ecc186

Observation 111b9753-ed5f-4a49-8c6d-1349bb516e05 · outbound

This paper cites Learning to predict by the methods of temporal differences.

Practical Risk Measures in Reinforcement Learning Learning to predict by the methods of temporal differences

Reference 2018

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T11:47:00.734699Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-14T11:47:00.448620Z digest=sha256:62d3f2016d1025104095a7104664b34a7a7f0ff5834d468fc337bd38df18dcff

Pith citing papers

No inbound Pith citation observations are available.