Paper Citation Record · LEDGER

Benchmarking Batch Deep Reinforcement Learning Algorithms

As of 23 August 2026, this Paper Citation Record lists 18 of 18 outbound references and 75 inbound Pith citation observations for arXiv:1910.01708.

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

pith.paper-citation-record.v1
1910.01708 v1

Coverage vector

measured 18 of 18 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-17T19:29:21.558661Z

measured 93 of 93 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00

measured 75 of 75 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T12:36:41.833362Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-04T19:30:07.900798Z

Reference resolution

18 of 18 outbound references displayed

verified exact10
verified fuzzy6
unresolved1
parse uncertain0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation f5c71f76-acb0-4258-8d31-5b61f67e85fa · outbound

This paper cites An Optimistic Perspective on Offline Reinforcement Learning.

Benchmarking Batch Deep Reinforcement Learning Algorithms An Optimistic Perspective on Offline Reinforcement Learning

Reference 1

Resolution: verified exact; arxiv_id, observed 2026-05-17T19:29:21.590209Z

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Observation 270e8bfe-f580-4423-b730-4c33b498c8e0 · outbound

This paper cites Exploration by Random Network Distillation.

Benchmarking Batch Deep Reinforcement Learning Algorithms Exploration by Random Network Distillation

Reference 2

Resolution: verified exact; local_arxiv, observed 2026-05-17T19:29:21.599744Z

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 38573401-85c1-45e1-9cf2-e47913c4d24c · outbound

This paper cites Dopamine: A Research Framework for Deep Reinforcement Learning.

Benchmarking Batch Deep Reinforcement Learning Algorithms Dopamine: A Research Framework for Deep Reinforcement Learning

Reference 3

Resolution: verified exact; local_arxiv, observed 2026-05-17T19:29:21.611689Z

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Observation c5238db0-d2c2-4881-8326-65a18ad5bebf · outbound

This paper cites Distributional Reinforcement Learning with Quantile Regression.

Benchmarking Batch Deep Reinforcement Learning Algorithms Distributional Reinforcement Learning with Quantile Regression

Reference 4

Resolution: verified exact; local_arxiv, observed 2026-05-17T19:29:21.618498Z

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Observation 4037aa9b-b0a9-4256-8a13-c2ca701c4aa1 · outbound

This paper cites Off-policy deep reinforcement learning without exploration.

Benchmarking Batch Deep Reinforcement Learning Algorithms Off-policy deep reinforcement learning without exploration

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T19:29:21.666629Z

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Observation 0aba04cd-9a8c-4beb-aaf0-e641fb6a70f4 · outbound

This paper cites Horizon: Facebook's Open Source Applied Reinforcement Learning Platform.

Benchmarking Batch Deep Reinforcement Learning Algorithms Horizon: Facebook's Open Source Applied Reinforcement Learning Platform

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-05-17T19:29:21.625100Z

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Observation 0645ee62-c2cb-4bf8-8b04-7a84ec627f0a · outbound

This paper cites Stable function approximation in dynamic programming.

Benchmarking Batch Deep Reinforcement Learning Algorithms Stable function approximation in dynamic programming

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T19:29:21.674078Z

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Observation a53f4dce-7733-4839-9ccb-0d0d27b6c8e8 · outbound

This paper cites Rainbow: Combining Improvements in Deep Reinforcement Learning.

Benchmarking Batch Deep Reinforcement Learning Algorithms Rainbow: Combining Improvements in Deep Reinforcement Learning

Reference 8

Resolution: verified exact; local_arxiv, observed 2026-05-17T19:29:21.631751Z

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 8ecc38f3-7f8c-4fed-88d4-79f0c81936f2 · outbound

This paper cites Way Off-Policy Batch Deep Reinforcement Learning of Implicit Human Preferences in Dialog.

Benchmarking Batch Deep Reinforcement Learning Algorithms Way Off-Policy Batch Deep Reinforcement Learning of Implicit Human Preferences in Dialog

Reference 9

Resolution: verified exact; local_arxiv, observed 2026-05-17T19:29:21.637970Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation d842aee0-fa97-44ef-8a4c-7f3a8b62cdf3 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Benchmarking Batch Deep Reinforcement Learning Algorithms Adam: A Method for Stochastic Optimization

Reference 10

Resolution: verified exact; local_arxiv, observed 2026-05-17T19:29:21.644578Z

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Observation 2898cbef-9b95-4710-abf4-7ae54f40de23 · outbound

This paper cites Stabilizing Off-Policy Q-Learning via Bootstrapping Error Reduction.

Benchmarking Batch Deep Reinforcement Learning Algorithms Stabilizing Off-Policy Q-Learning via Bootstrapping Error Reduction

Reference 11

Resolution: metadata mismatch; arxiv_id, observed 2026-05-17T19:29:21.650917Z

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 9d6aff67-f8ff-41f2-8fe6-f18b8d49dc25 · outbound

This paper cites Continuous control with deep reinforcement learning.

Benchmarking Batch Deep Reinforcement Learning Algorithms Continuous control with deep reinforcement learning

Reference 12

Resolution: verified exact; local_arxiv, observed 2026-05-17T19:29:21.656717Z

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Observation ce67a85b-069e-40d6-844f-a71c3285e649 · outbound

This paper cites Safe Policy Improvement with an Estimated Baseline Policy.

Benchmarking Batch Deep Reinforcement Learning Algorithms Safe Policy Improvement with an Estimated Baseline Policy

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-05-17T19:29:21.662602Z

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation cb6f1ee4-bccf-47ae-b827-6e3fb5c8789d · outbound

This paper cites Dropout: a simple way to prevent neural networks from overﬁtting.

Benchmarking Batch Deep Reinforcement Learning Algorithms Dropout: a simple way to prevent neural networks from overﬁtting

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T19:29:21.681274Z

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Observation 50465d77-4b46-4af1-b0c0-b30612ec889d · outbound

This paper cites Issues in using function approximation for reinforcement learning.

Benchmarking Batch Deep Reinforcement Learning Algorithms Issues in using function approximation for reinforcement learning

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T19:29:21.684791Z

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Observation 6c80d18f-b833-4539-9c3f-26ef7f04f701 · outbound

This paper cites Deep reinforcement learning with double q- learning.

Benchmarking Batch Deep Reinforcement Learning Algorithms Deep reinforcement learning with double q- learning

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T19:29:21.688632Z

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Observation b4b4e93e-f71b-4152-a014-c9282939f8cc · outbound

This paper cites an unresolved cited work.

Benchmarking Batch Deep Reinforcement Learning Algorithms Unresolved cited work

Reference 17

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation c231a451-7b55-4a9b-ac67-165dff134b61 · outbound

This paper cites Table 1: Hyper-parameters used by each network.

Benchmarking Batch Deep Reinforcement Learning Algorithms Table 1: Hyper-parameters used by each network

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T19:29:21.677555Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Pith citing papers

Observation e568771c-86df-498e-9c1c-d17c6d0e787f · inbound

Analyzing Adversarial Inputs in Deep Reinforcement Learning cites this paper.

Analyzing Adversarial Inputs in Deep Reinforcement Learning Benchmarking Batch Deep Reinforcement Learning Algorithms

Reference 16

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Source-reported events for the cited work

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Observation 10774b93-9586-4dc5-adb2-f478da525fe6 · inbound

An Investigation of Offline Reinforcement Learning in Factorisable Action Spaces cites this paper.

An Investigation of Offline Reinforcement Learning in Factorisable Action Spaces Benchmarking Batch Deep Reinforcement Learning Algorithms

Reference 16

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Observation 4db3688e-30cd-44f8-b1ed-664c158916af · inbound

Safe Multi-Agent Reinforcement Learning with Convergence to Generalized Nash Equilibrium cites this paper.

Safe Multi-Agent Reinforcement Learning with Convergence to Generalized Nash Equilibrium Benchmarking Batch Deep Reinforcement Learning Algorithms

Reference 43

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Observation 402a4b1c-c5b2-4101-9cfa-b4172a00738e · inbound

TEA: Trajectory Encoding Augmentation for Robust and Transferable Policies in Offline Reinforcement Learning cites this paper.

TEA: Trajectory Encoding Augmentation for Robust and Transferable Policies in Offline Reinforcement Learning Benchmarking Batch Deep Reinforcement Learning Algorithms

Reference 8

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Observation 601e906b-7322-4f7b-b574-398969d00804 · inbound

A Dynamic Safety Shield for Safe and Efficient Reinforcement Learning of Navigation Tasks cites this paper.

A Dynamic Safety Shield for Safe and Efficient Reinforcement Learning of Navigation Tasks Benchmarking Batch Deep Reinforcement Learning Algorithms

Reference 14

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Observation 96f35f8a-f1fd-4bb7-b509-1ae6ea2e49c5 · inbound

Action Mapping for Reinforcement Learning in Continuous Environments with Constraints cites this paper.

Action Mapping for Reinforcement Learning in Continuous Environments with Constraints Benchmarking Batch Deep Reinforcement Learning Algorithms

Reference 9

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Effective Reward Specification in Deep Reinforcement Learning cites this paper.

Effective Reward Specification in Deep Reinforcement Learning Benchmarking Batch Deep Reinforcement Learning Algorithms

Reference 257

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From Text to Trajectory: Exploring Complex Constraint Representation and Decomposition in Safe Reinforcement Learning cites this paper.

From Text to Trajectory: Exploring Complex Constraint Representation and Decomposition in Safe Reinforcement Learning Benchmarking Batch Deep Reinforcement Learning Algorithms

Reference 6

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Deep Reinforcement Learning for Scalable Multiagent Spacecraft Inspection cites this paper.

Deep Reinforcement Learning for Scalable Multiagent Spacecraft Inspection Benchmarking Batch Deep Reinforcement Learning Algorithms

Reference 8

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Safe Reinforcement Learning using Finite-Horizon Gradient-based Estimation cites this paper.

Safe Reinforcement Learning using Finite-Horizon Gradient-based Estimation Benchmarking Batch Deep Reinforcement Learning Algorithms

Reference 47

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Offline Safe Reinforcement Learning Using Trajectory Classification cites this paper.

Offline Safe Reinforcement Learning Using Trajectory Classification Benchmarking Batch Deep Reinforcement Learning Algorithms

Reference 9

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Constraint-Adaptive Policy Switching for Offline Safe Reinforcement Learning cites this paper.

Constraint-Adaptive Policy Switching for Offline Safe Reinforcement Learning Benchmarking Batch Deep Reinforcement Learning Algorithms

Reference 28

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Autonomous Alignment with Human Value on Altruism through Considerate Self-imagination and Theory of Mind cites this paper.

Autonomous Alignment with Human Value on Altruism through Considerate Self-imagination and Theory of Mind Benchmarking Batch Deep Reinforcement Learning Algorithms

Reference 30

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Scalable Safe Multi-Agent Reinforcement Learning for Multi-Agent System cites this paper.

Scalable Safe Multi-Agent Reinforcement Learning for Multi-Agent System Benchmarking Batch Deep Reinforcement Learning Algorithms

Reference 20

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DIAL: Distribution-Informed Adaptive Learning of Multi-Task Constraints for Safety-Critical Systems cites this paper.

DIAL: Distribution-Informed Adaptive Learning of Multi-Task Constraints for Safety-Critical Systems Benchmarking Batch Deep Reinforcement Learning Algorithms

Reference 50

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Enhancing Offline Reinforcement Learning with Curriculum Learning-Based Trajectory Valuation cites this paper.

Enhancing Offline Reinforcement Learning with Curriculum Learning-Based Trajectory Valuation Benchmarking Batch Deep Reinforcement Learning Algorithms

Reference 2019

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Vulnerability Mitigation for Safety-Aligned Language Models via Debiasing cites this paper.

Vulnerability Mitigation for Safety-Aligned Language Models via Debiasing Benchmarking Batch Deep Reinforcement Learning Algorithms

Reference 38

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TraCeS: Learning Per-Timestep Constraint-Violation Credit from Sparse Trajectory-Level Labels cites this paper.

TraCeS: Learning Per-Timestep Constraint-Violation Credit from Sparse Trajectory-Level Labels Benchmarking Batch Deep Reinforcement Learning Algorithms

Reference 22

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Safe Primal-Dual Optimization with a Single Smooth Constraint cites this paper.

Safe Primal-Dual Optimization with a Single Smooth Constraint Benchmarking Batch Deep Reinforcement Learning Algorithms

Reference 15

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Ctrl-DNA: Controllable Cell-Type-Specific Regulatory DNA Design via Constrained RL cites this paper.

Ctrl-DNA: Controllable Cell-Type-Specific Regulatory DNA Design via Constrained RL Benchmarking Batch Deep Reinforcement Learning Algorithms

Reference 12

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Central Path Proximal Policy Optimization cites this paper.

Central Path Proximal Policy Optimization Benchmarking Batch Deep Reinforcement Learning Algorithms

Reference 2022

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Modular Diffusion Policy Training: Decoupling and Recombining Guidance and Diffusion for Offline RL cites this paper.

Modular Diffusion Policy Training: Decoupling and Recombining Guidance and Diffusion for Offline RL Benchmarking Batch Deep Reinforcement Learning Algorithms

Reference 15

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Robot-Gated Interactive Imitation Learning with Adaptive Intervention Mechanism cites this paper.

Robot-Gated Interactive Imitation Learning with Adaptive Intervention Mechanism Benchmarking Batch Deep Reinforcement Learning Algorithms

Reference 12

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Joint User Priority and Power Scheduling for QoS-Aware WMMSE Precoding: A Constrained-Actor Attentive-Critic Approach cites this paper.

Joint User Priority and Power Scheduling for QoS-Aware WMMSE Precoding: A Constrained-Actor Attentive-Critic Approach Benchmarking Batch Deep Reinforcement Learning Algorithms

Reference 11

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A Survey on Autonomy-Induced Security Risks in Large Model-Based Agents cites this paper.

A Survey on Autonomy-Induced Security Risks in Large Model-Based Agents Benchmarking Batch Deep Reinforcement Learning Algorithms

Reference 120

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PNAct: Crafting Backdoor Attacks in Safe Reinforcement Learning cites this paper.

PNAct: Crafting Backdoor Attacks in Safe Reinforcement Learning Benchmarking Batch Deep Reinforcement Learning Algorithms

Reference 10

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Red-Team Multi-Agent Reinforcement Learning for Emergency Braking Scenario cites this paper.

Red-Team Multi-Agent Reinforcement Learning for Emergency Braking Scenario Benchmarking Batch Deep Reinforcement Learning Algorithms

Reference 20

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Safe Deployment of Offline Reinforcement Learning via Input Convex Action Correction cites this paper.

Safe Deployment of Offline Reinforcement Learning via Input Convex Action Correction Benchmarking Batch Deep Reinforcement Learning Algorithms

Reference 27

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A Review On Safe Reinforcement Learning Using Lyapunov and Barrier Functions cites this paper.

A Review On Safe Reinforcement Learning Using Lyapunov and Barrier Functions Benchmarking Batch Deep Reinforcement Learning Algorithms

Reference 129

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Incentivizing Safer Actions in Policy Optimization for Constrained Reinforcement Learning cites this paper.

Incentivizing Safer Actions in Policy Optimization for Constrained Reinforcement Learning Benchmarking Batch Deep Reinforcement Learning Algorithms

Reference 22

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Constraint-Aware Reinforcement Learning via Adaptive Action Scaling cites this paper.

Constraint-Aware Reinforcement Learning via Adaptive Action Scaling Benchmarking Batch Deep Reinforcement Learning Algorithms

Reference 16

Resolution: verified exact; local_arxiv, observed 2026-05-18T07:41:03.345959Z

Source-reported events for the cited work

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Observation 43ca3ec6-2bf1-47a1-a028-fb0b8ae43eb3 · inbound

A Review of Learning-Based Motion Planning: Toward a Data-Driven Optimal Control Approach cites this paper.

A Review of Learning-Based Motion Planning: Toward a Data-Driven Optimal Control Approach Benchmarking Batch Deep Reinforcement Learning Algorithms

Reference 47

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Observation 3685c4cd-4d4c-40e1-b98d-06dcfc111f1c · inbound

MoralityGym: A Benchmark for Evaluating Hierarchical Moral Alignment in Sequential Decision-Making Agents cites this paper.

MoralityGym: A Benchmark for Evaluating Hierarchical Moral Alignment in Sequential Decision-Making Agents Benchmarking Batch Deep Reinforcement Learning Algorithms

Reference 79

Resolution: verified exact; local_arxiv, observed 2026-05-22T10:51:25.963446Z

Source-reported events for the cited work

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Observation ae766d1d-e08d-4457-a588-2b9920ed99b1 · inbound

RVN-Bench: A Benchmark for Reactive Visual Navigation cites this paper.

RVN-Bench: A Benchmark for Reactive Visual Navigation Benchmarking Batch Deep Reinforcement Learning Algorithms

Reference 18

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Fatigue-Aware Learning to Defer via Constrained Optimisation cites this paper.

Fatigue-Aware Learning to Defer via Constrained Optimisation Benchmarking Batch Deep Reinforcement Learning Algorithms

Reference 34

Resolution: verified exact; arxiv_id, observed 2026-05-17T19:29:21.689617Z

Source-reported events for the cited work

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Observation 7cd178e8-a547-4bb8-b0aa-78ebced09910 · inbound

Improving Feasibility via Fast Autoencoder-Based Projections cites this paper.

Improving Feasibility via Fast Autoencoder-Based Projections Benchmarking Batch Deep Reinforcement Learning Algorithms

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-05-17T19:29:21.689617Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 59f7449d-b563-4ec3-86cf-cffb7c9d3c12 · inbound

Pedagogical Safety in Educational Reinforcement Learning: Formalizing and Detecting Reward Hacking in AI Tutoring Systems cites this paper.

Pedagogical Safety in Educational Reinforcement Learning: Formalizing and Detecting Reward Hacking in AI Tutoring Systems Benchmarking Batch Deep Reinforcement Learning Algorithms

Reference 60

Resolution: unresolved; no resolver link, observed 2026-07-13T10:37:39.719847Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b751cafb-209d-4cd3-acb2-ce23f9db898e · inbound

CMP: Robust Whole-Body Tracking for Loco-Manipulation via Competence Manifold Projection cites this paper.

CMP: Robust Whole-Body Tracking for Loco-Manipulation via Competence Manifold Projection Benchmarking Batch Deep Reinforcement Learning Algorithms

Reference 38

Resolution: verified exact; arxiv_id, observed 2026-05-17T19:29:21.689617Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 15b761da-e125-49c1-b1b8-d4b3cea2f781 · inbound

Safe reinforcement learning with online filtering for fatigue-predictive human-robot task planning and allocation in production cites this paper.

Safe reinforcement learning with online filtering for fatigue-predictive human-robot task planning and allocation in production Benchmarking Batch Deep Reinforcement Learning Algorithms

Reference 67

Resolution: verified exact; arxiv_id, observed 2026-05-17T19:29:21.689617Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 8e74a425-c637-49bf-9585-35394d799919 · inbound

CAPSULE: Control-Theoretic Action Perturbations for Safe Uncertainty-Aware Reinforcement Learning cites this paper.

CAPSULE: Control-Theoretic Action Perturbations for Safe Uncertainty-Aware Reinforcement Learning Benchmarking Batch Deep Reinforcement Learning Algorithms

Reference 9

Resolution: metadata mismatch; arxiv_id, observed 2026-05-17T19:29:21.689617Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation fb937938-e8a1-4f07-9f61-59a2006b624c · inbound

Augmented Lagrangian Multiplier Network for State-wise Safety in Reinforcement Learning cites this paper.

Augmented Lagrangian Multiplier Network for State-wise Safety in Reinforcement Learning Benchmarking Batch Deep Reinforcement Learning Algorithms

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-05-17T19:29:21.689617Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation e779dbca-7a79-428a-a1de-3916ca5ada33 · inbound

When Life Gives You BC, Make Q-functions: Extracting Q-values from Behavior Cloning for On-Robot Reinforcement Learning cites this paper.

When Life Gives You BC, Make Q-functions: Extracting Q-values from Behavior Cloning for On-Robot Reinforcement Learning Benchmarking Batch Deep Reinforcement Learning Algorithms

Reference 69

Resolution: metadata mismatch; arxiv_id, observed 2026-05-17T19:29:21.689617Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation b301ab83-4438-49ac-8af7-1f2ce61b8075 · inbound

When Life Gives You BC, Make Q-functions: Extracting Q-values from Behavior Cloning for On-Robot Reinforcement Learning cites this paper.

When Life Gives You BC, Make Q-functions: Extracting Q-values from Behavior Cloning for On-Robot Reinforcement Learning Benchmarking Batch Deep Reinforcement Learning Algorithms

Reference 9

Resolution: verified exact; local_arxiv, observed 2026-06-30T23:25:07.011373Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation b465eb59-5652-4688-b0b1-4887c2b5b33d · inbound

Semantic State Abstraction Interfaces for LLM-Augmented Portfolio Decisions: Multi-Axis News Decomposition and RL Diagnostics cites this paper.

Semantic State Abstraction Interfaces for LLM-Augmented Portfolio Decisions: Multi-Axis News Decomposition and RL Diagnostics Benchmarking Batch Deep Reinforcement Learning Algorithms

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-05-17T19:29:21.689617Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation bb6e9414-5957-4b52-9384-1c9d22bfa0a2 · inbound

Learned Lyapunov Shielding for Adaptive Control cites this paper.

Learned Lyapunov Shielding for Adaptive Control Benchmarking Batch Deep Reinforcement Learning Algorithms

Reference 24

Resolution: verified exact; arxiv_id, observed 2026-05-17T19:29:21.689617Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 23d99d08-474a-4b3a-ab94-bfc037e87d44 · inbound

Why Does Agentic Safety Fail to Generalize Across Tasks? cites this paper.

Why Does Agentic Safety Fail to Generalize Across Tasks? Benchmarking Batch Deep Reinforcement Learning Algorithms

Reference 93

Resolution: verified exact; arxiv_id, observed 2026-05-17T19:29:21.689617Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 6b4d1d4c-fc43-4979-97a0-04b888d5fd60 · inbound

Shaping Zero-Shot Coordination via State Blocking cites this paper.

Shaping Zero-Shot Coordination via State Blocking Benchmarking Batch Deep Reinforcement Learning Algorithms

Reference 40

Resolution: verified exact; arxiv_id, observed 2026-05-17T19:29:21.689617Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 5e11f91c-3fe9-4a78-8dcc-c908d31193c3 · inbound

Augmented Lagrangian Method for Last-Iterate Convergence for Constrained MDPs cites this paper.

Augmented Lagrangian Method for Last-Iterate Convergence for Constrained MDPs Benchmarking Batch Deep Reinforcement Learning Algorithms

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-05-17T19:29:21.689617Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation af15332e-7b74-47fc-9557-f7b69ab34744 · inbound

Stochastic Minimum-Cost Reach-Avoid Reinforcement Learning cites this paper.

Stochastic Minimum-Cost Reach-Avoid Reinforcement Learning Benchmarking Batch Deep Reinforcement Learning Algorithms

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-05-17T19:29:21.689617Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 97bb7f74-5570-410e-99d8-fecde9a46bfc · inbound

Stochastic Minimum-Cost Reach-Avoid Reinforcement Learning cites this paper.

Stochastic Minimum-Cost Reach-Avoid Reinforcement Learning Benchmarking Batch Deep Reinforcement Learning Algorithms

Reference 8

Resolution: verified exact; local_arxiv, observed 2026-05-20T22:49:10.063584Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 38f0fb72-863f-40f9-8620-18fe86dc24e1 · inbound

Optimal design of solar-battery hybrid resources considering multi-market participation under weather and price uncertainty cites this paper.

Optimal design of solar-battery hybrid resources considering multi-market participation under weather and price uncertainty Benchmarking Batch Deep Reinforcement Learning Algorithms

Reference 26

Resolution: verified exact; arxiv_id, observed 2026-05-17T19:29:21.689617Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 7d0137ea-c6eb-4324-a1e3-2a6a45929180 · inbound

Safety-Constrained Reinforcement Learning with Post-Training Reachability Verification for Robot Navigation cites this paper.

Safety-Constrained Reinforcement Learning with Post-Training Reachability Verification for Robot Navigation Benchmarking Batch Deep Reinforcement Learning Algorithms

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-05-17T19:29:21.689617Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation f66ff6a4-cf26-478c-a5b5-3a132ecf6470 · inbound

Action-Conditioned Risk Gating for Safety-Critical Control under Partial Observability cites this paper.

Action-Conditioned Risk Gating for Safety-Critical Control under Partial Observability Benchmarking Batch Deep Reinforcement Learning Algorithms

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-05-17T19:29:21.689617Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation c3c9f68c-d675-4e85-898c-3fe9d2c47a5e · inbound

Generative Auto-Bidding with Unified Modeling and Exploration cites this paper.

Generative Auto-Bidding with Unified Modeling and Exploration Benchmarking Batch Deep Reinforcement Learning Algorithms

Reference 13

Resolution: verified exact; local_arxiv, observed 2026-05-20T05:38:05.223920Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation b980eb7d-833c-46b2-bd15-e9963f6a5b42 · inbound

Generative OOD-regularized Model-based Policy Optimization cites this paper.

Generative OOD-regularized Model-based Policy Optimization Benchmarking Batch Deep Reinforcement Learning Algorithms

Reference 14

Resolution: verified exact; local_arxiv, observed 2026-06-30T14:44:45.094477Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation fc9d92df-4334-4bcd-a42d-310fcb0242fc · inbound

RCSP: Risk-Sensitive Conjectural Scenario Planning for Safe Dynamic Robot Navigation cites this paper.

RCSP: Risk-Sensitive Conjectural Scenario Planning for Safe Dynamic Robot Navigation Benchmarking Batch Deep Reinforcement Learning Algorithms

Reference 14

Resolution: verified exact; local_arxiv, observed 2026-06-29T22:14:00.560615Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation b9589d97-1050-4eea-b859-7a76ef6f6d7f · inbound

Policy-Conditioned Counterfactual Credit for Verifiable Reinforcement Learning of Long-Horizon Language Agents cites this paper.

Policy-Conditioned Counterfactual Credit for Verifiable Reinforcement Learning of Long-Horizon Language Agents Benchmarking Batch Deep Reinforcement Learning Algorithms

Reference 43

Resolution: metadata mismatch; local_arxiv, observed 2026-07-02T06:06:41.598471Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 9fd9b573-b02a-401f-a5ba-fb9b13bfb157 · inbound

TRIDENT: Breaking the Hybrid-Safety-Physics Coupling for Provably Safe Multi-Agent Reinforcement Learning cites this paper.

TRIDENT: Breaking the Hybrid-Safety-Physics Coupling for Provably Safe Multi-Agent Reinforcement Learning Benchmarking Batch Deep Reinforcement Learning Algorithms

Reference 79

Resolution: verified exact; local_arxiv, observed 2026-07-03T19:18:54.937345Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 7f5b16a5-9a59-40d9-9bca-036bae8ef484 · inbound

CRAX: Fast Safe Reinforcement Learning Benchmarking cites this paper.

CRAX: Fast Safe Reinforcement Learning Benchmarking Benchmarking Batch Deep Reinforcement Learning Algorithms

Reference 31

Resolution: verified exact; local_arxiv, observed 2026-07-04T03:09:30.098710Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation e698c42b-0500-4172-af39-ebb2c0abd390 · inbound

Imagine to Ensure Safety in Hierarchical Reinforcement Learning cites this paper.

Imagine to Ensure Safety in Hierarchical Reinforcement Learning Benchmarking Batch Deep Reinforcement Learning Algorithms

Reference 1

Resolution: verified exact; local_arxiv, observed 2026-07-04T08:59:42.179239Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation df0a08a4-fc2a-4276-b45d-a5cb9f8001be · inbound

Average-Power-Budgeted Underwater Vehicle Control via Constrained Reinforcement Learning cites this paper.

Average-Power-Budgeted Underwater Vehicle Control via Constrained Reinforcement Learning Benchmarking Batch Deep Reinforcement Learning Algorithms

Reference 51

Resolution: metadata mismatch; local_arxiv, observed 2026-07-04T19:30:07.902034Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation e8cd97e5-069e-43df-b886-01e995a3db53 · inbound

Average-Power-Budgeted Underwater Vehicle Control via Constrained Reinforcement Learning cites this paper.

Average-Power-Budgeted Underwater Vehicle Control via Constrained Reinforcement Learning Benchmarking Batch Deep Reinforcement Learning Algorithms

Reference 6

Resolution: unresolved; no resolver link, observed 2026-07-14T17:22:01.242752Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5a7a5f7e-4b67-4e41-b1cb-0a6987a21110 · inbound

ForesightSafety-VLA: A Unified Diagnostic Safety Benchmark for Vision-Language-Action Models cites this paper.

ForesightSafety-VLA: A Unified Diagnostic Safety Benchmark for Vision-Language-Action Models Benchmarking Batch Deep Reinforcement Learning Algorithms

Reference 20

Resolution: verified exact; local_arxiv, observed 2026-07-04T13:29:51.836696Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 8fa64724-0c3f-4bfb-9582-b0889b6ca1eb · inbound

ForesightSafety-VLA: A Unified Diagnostic Safety Benchmark for Vision-Language-Action Models cites this paper.

ForesightSafety-VLA: A Unified Diagnostic Safety Benchmark for Vision-Language-Action Models Benchmarking Batch Deep Reinforcement Learning Algorithms

Reference 20

Resolution: verified exact; local_arxiv, observed 2026-06-30T09:54:34.851041Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 7ee01f2c-7765-48d8-9460-9b2f934d0a60 · inbound

PPO-EAL: Exact Augmented Lagrangian Proximal Policy Optimization for Safe Robotic Control cites this paper.

PPO-EAL: Exact Augmented Lagrangian Proximal Policy Optimization for Safe Robotic Control Benchmarking Batch Deep Reinforcement Learning Algorithms

Reference 16

Resolution: verified exact; local_arxiv, observed 2026-06-29T19:13:53.381758Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 637a075c-bb27-4f0c-89b6-dc5bcad91686 · inbound

Safe Online Learning via Smooth Safety-Structured Policy Composition cites this paper.

Safe Online Learning via Smooth Safety-Structured Policy Composition Benchmarking Batch Deep Reinforcement Learning Algorithms

Reference 12

Resolution: malformed identifier; local_arxiv, observed 2026-07-01T09:45:40.506809Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 94e178dc-7981-4ea0-8e55-5c4db24dcc9a · inbound

OopsieVerse: A Safety Benchmark with Damage-Aware Simulation for Robot Manipulation cites this paper.

OopsieVerse: A Safety Benchmark with Damage-Aware Simulation for Robot Manipulation Benchmarking Batch Deep Reinforcement Learning Algorithms

Reference 20

Resolution: verified exact; local_arxiv, observed 2026-07-01T10:45:43.040440Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 44759c31-c891-4ed8-af44-f173415ead9c · inbound

Active Sensing for RIS-Aided Tracking and Power Control: A Hybrid Neuroevolution and Supervised Learning Approach cites this paper.

Active Sensing for RIS-Aided Tracking and Power Control: A Hybrid Neuroevolution and Supervised Learning Approach Benchmarking Batch Deep Reinforcement Learning Algorithms

Reference 68

Resolution: verified exact; local_arxiv, observed 2026-07-02T17:47:17.440985Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation c410a26a-eb3d-4913-aeb1-590e5959ffd1 · inbound

Active Sensing for RIS-Aided Tracking and Power Control: A Hybrid Neuroevolution and Supervised Learning Approach cites this paper.

Active Sensing for RIS-Aided Tracking and Power Control: A Hybrid Neuroevolution and Supervised Learning Approach Benchmarking Batch Deep Reinforcement Learning Algorithms

Reference 68

Resolution: verified exact; local_arxiv, observed 2026-07-03T22:29:00.224900Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 761f3ebe-82a2-4654-98b5-51709527c71f · inbound

Fused Constrained Policy Reuse Optimization for Wireless Resource Allocation cites this paper.

Fused Constrained Policy Reuse Optimization for Wireless Resource Allocation Benchmarking Batch Deep Reinforcement Learning Algorithms

Reference 31

Resolution: unresolved; no resolver link, observed 2026-07-13T02:36:27.505388Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 492df8cf-689d-419a-b69c-0b7cfeb71747 · inbound

Towards Trustworthy Embodied Intelligence: A Systems Framework and Graded Trustworthiness Levels cites this paper.

Towards Trustworthy Embodied Intelligence: A Systems Framework and Graded Trustworthiness Levels Benchmarking Batch Deep Reinforcement Learning Algorithms

Reference 85

Resolution: unresolved; no resolver link, observed 2026-08-01T00:48:45.749486Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2763f45d-b120-4eb2-b30d-03bcd913fe23 · inbound

Security of World-Model-Based Embodied AI: A Lifecycle of Threats, Defenses, and Evaluation cites this paper.

Security of World-Model-Based Embodied AI: A Lifecycle of Threats, Defenses, and Evaluation Benchmarking Batch Deep Reinforcement Learning Algorithms

Reference 157

Resolution: unresolved; no resolver link, observed 2026-07-31T14:03:37.225305Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1a0de04b-cf77-4da2-b511-dfac7aaf53c7 · inbound

EntailLLM: Verifying LLM-Generated Vulnerability Discovery Paths with Domain Knowledge via Logic Programming cites this paper.

EntailLLM: Verifying LLM-Generated Vulnerability Discovery Paths with Domain Knowledge via Logic Programming Benchmarking Batch Deep Reinforcement Learning Algorithms

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-04T21:36:16.177038Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a7a5b947-86cf-4c11-b9e4-2c2b6c079aba · inbound

Navigating the Proximity-Safety Balance: Constraint Decomposition for Human Following in Pedestrian Crowds cites this paper.

Navigating the Proximity-Safety Balance: Constraint Decomposition for Human Following in Pedestrian Crowds Benchmarking Batch Deep Reinforcement Learning Algorithms

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-14T04:21:32.228188Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 25f95649-afec-4334-8810-b788610e90f5 · inbound

Boundary-Seeking Policy Gradient for Safe Reinforcement Learning cites this paper.

Boundary-Seeking Policy Gradient for Safe Reinforcement Learning Benchmarking Batch Deep Reinforcement Learning Algorithms

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-14T04:18:18.373663Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.