Paper Citation Record · LEDGER

A Survey On Enhancing Reinforcement Learning in Complex Environments: Insights from Human and LLM Feedback

As of 13 August 2026, this Paper Citation Record lists 83 of 83 outbound references and 4 inbound Pith citation observations for arXiv:2411.13410.

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

pith.paper-citation-record.v1
2411.13410 v1

Coverage vector

measured 83 of 83 reference resolution

Typed states for the displayed outbound observations.

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measured 87 of 87 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:49:31.040589Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-22T21:52:10.203651Z

Reference resolution

83 of 83 outbound references displayed

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External citation measurements

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Outbound references

Observation dba0a613-d316-4f9d-b94a-a0cbe8c90065 · outbound

This paper cites Reinforcement learning in healthcare: A survey.

A Survey On Enhancing Reinforcement Learning in Complex Environments: Insights from Human and LLM Feedback Reinforcement learning in healthcare: A survey

Reference 1

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Observation ece67a6e-c4cb-43ec-8111-f4434523d3c8 · outbound

This paper cites Survey on reinforcement learning for language processing.

A Survey On Enhancing Reinforcement Learning in Complex Environments: Insights from Human and LLM Feedback Survey on reinforcement learning for language processing

Reference 2

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Observation d533b9b4-7d0f-4a14-823f-c0f7d05e0879 · outbound

This paper cites Recent advances in reinforcement learning in finance.Mathematical Finance, 33(3):437–503, 2023.

A Survey On Enhancing Reinforcement Learning in Complex Environments: Insights from Human and LLM Feedback Recent advances in reinforcement learning in finance.Mathematical Finance, 33(3):437–503, 2023

Reference 3

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Observation b0a1a4b4-6abd-4a3b-8a96-033b2393db73 · outbound

This paper cites Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback.

A Survey On Enhancing Reinforcement Learning in Complex Environments: Insights from Human and LLM Feedback Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback

Reference 4

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Observation 912ce50e-cab6-4bf8-aea1-874afb8b87fd · outbound

This paper cites A review on interactive reinforcement learning from human social feedback.

A Survey On Enhancing Reinforcement Learning in Complex Environments: Insights from Human and LLM Feedback A review on interactive reinforcement learning from human social feedback

Reference 5

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Observation 66592c6e-5ce4-451a-8cb7-a8ab2e6d2eaf · outbound

This paper cites Assessing Generalization in Deep Reinforcement Learning.

A Survey On Enhancing Reinforcement Learning in Complex Environments: Insights from Human and LLM Feedback Assessing Generalization in Deep Reinforcement Learning

Reference 6

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Observation 8601ff7b-f855-4927-9ca3-e85480ff2dfb · outbound

This paper cites Latent exploration for reinforcement learning.

A Survey On Enhancing Reinforcement Learning in Complex Environments: Insights from Human and LLM Feedback Latent exploration for reinforcement learning

Reference 7

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Observation b3a785be-45c8-44f4-9892-1199dc287144 · outbound

This paper cites Reinforcement learning: A tutorial survey and recent advances.

A Survey On Enhancing Reinforcement Learning in Complex Environments: Insights from Human and LLM Feedback Reinforcement learning: A tutorial survey and recent advances

Reference 8

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Observation 779a3c90-4ab6-443f-9b0b-c1e44a71992a · outbound

This paper cites A survey of reinforcement learning from human feedback.

A Survey On Enhancing Reinforcement Learning in Complex Environments: Insights from Human and LLM Feedback A survey of reinforcement learning from human feedback

Reference 9

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Observation 0af62018-f84e-4e15-9d07-0ce3d9c9c4aa · outbound

This paper cites The RL/LLM Taxonomy Tree: Reviewing Synergies Between Reinforcement Learning and Large Language Models.

A Survey On Enhancing Reinforcement Learning in Complex Environments: Insights from Human and LLM Feedback The RL/LLM Taxonomy Tree: Reviewing Synergies Between Reinforcement Learning and Large Language Models

Reference 10

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Observation 7d1a0ee0-36f0-4ead-9e5e-827218367201 · outbound

This paper cites A conceptual framework for externally-influenced agents: An assisted reinforcement learning review.

A Survey On Enhancing Reinforcement Learning in Complex Environments: Insights from Human and LLM Feedback A conceptual framework for externally-influenced agents: An assisted reinforcement learning review

Reference 11

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Observation 46328215-0d1b-4d7a-9e74-0cef46f6e311 · outbound

This paper cites Language as an abstraction for hierarchical deep reinforcement learning.

A Survey On Enhancing Reinforcement Learning in Complex Environments: Insights from Human and LLM Feedback Language as an abstraction for hierarchical deep reinforcement learning

Reference 12

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This paper cites Beating Atari with Natural Language Guided Reinforcement Learning.

A Survey On Enhancing Reinforcement Learning in Complex Environments: Insights from Human and LLM Feedback Beating Atari with Natural Language Guided Reinforcement Learning

Reference 13

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This paper cites The arcade learning environment: An evaluation platform for general agents.

A Survey On Enhancing Reinforcement Learning in Complex Environments: Insights from Human and LLM Feedback The arcade learning environment: An evaluation platform for general agents

Reference 14

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Observation 9a960bfc-8359-4e6c-a399-c0d6a5c680fa · outbound

This paper cites Human Instruction-Following with Deep Reinforcement Learning via Transfer-Learning from Text.

A Survey On Enhancing Reinforcement Learning in Complex Environments: Insights from Human and LLM Feedback Human Instruction-Following with Deep Reinforcement Learning via Transfer-Learning from Text

Reference 15

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Observation 73d691cd-61f9-46eb-a063-a0b105de59da · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

A Survey On Enhancing Reinforcement Learning in Complex Environments: Insights from Human and LLM Feedback BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 16

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This paper cites Deep reinforcement learning for instruction following visual navigation in 3d maze-like environments.

A Survey On Enhancing Reinforcement Learning in Complex Environments: Insights from Human and LLM Feedback Deep reinforcement learning for instruction following visual navigation in 3d maze-like environments

Reference 17

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Observation 29ab2f82-cc7c-4ad6-81bd-1e3022f00a89 · outbound

This paper cites Learning to follow directions in street view.

A Survey On Enhancing Reinforcement Learning in Complex Environments: Insights from Human and LLM Feedback Learning to follow directions in street view

Reference 18

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Observation a596834c-90ed-4a55-9c85-5aae50cc25ce · outbound

This paper cites Language Instructed Reinforcement Learning for Human-AI Coordination.

A Survey On Enhancing Reinforcement Learning in Complex Environments: Insights from Human and LLM Feedback Language Instructed Reinforcement Learning for Human-AI Coordination

Reference 19

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This paper cites Ask Your Humans: Using Human Instructions to Improve Generalization in Reinforcement Learning.

A Survey On Enhancing Reinforcement Learning in Complex Environments: Insights from Human and LLM Feedback Ask Your Humans: Using Human Instructions to Improve Generalization in Reinforcement Learning

Reference 20

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This paper cites Meta-reinforcement learning via language instructions.

A Survey On Enhancing Reinforcement Learning in Complex Environments: Insights from Human and LLM Feedback Meta-reinforcement learning via language instructions

Reference 21

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Observation ee46c1be-a28f-4b45-8c27-9803dbbf5d49 · outbound

This paper cites Guiding multi-step rearrangement tasks with natural language instructions.

A Survey On Enhancing Reinforcement Learning in Complex Environments: Insights from Human and LLM Feedback Guiding multi-step rearrangement tasks with natural language instructions

Reference 22

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Observation 7a4ab7f4-e985-4296-ac73-e358b99baebd · outbound

This paper cites Interactive language: Talking to robots in real time.

A Survey On Enhancing Reinforcement Learning in Complex Environments: Insights from Human and LLM Feedback Interactive language: Talking to robots in real time

Reference 23

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This paper cites Correcting Robot Plans with Natural Language Feedback.

A Survey On Enhancing Reinforcement Learning in Complex Environments: Insights from Human and LLM Feedback Correcting Robot Plans with Natural Language Feedback

Reference 24

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This paper cites Yell At Your Robot: Improving On-the-Fly from Language Corrections.

A Survey On Enhancing Reinforcement Learning in Complex Environments: Insights from Human and LLM Feedback Yell At Your Robot: Improving On-the-Fly from Language Corrections

Reference 25

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A Survey On Enhancing Reinforcement Learning in Complex Environments: Insights from Human and LLM Feedback Continual learning for instruction following from realtime feedback

Reference 26

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Observation 39ed1bf9-07e2-4e03-9a01-45f86e252ca8 · outbound

This paper cites Incorporating Voice Instructions in Model-Based Reinforcement Learning for Self-Driving Cars.

A Survey On Enhancing Reinforcement Learning in Complex Environments: Insights from Human and LLM Feedback Incorporating Voice Instructions in Model-Based Reinforcement Learning for Self-Driving Cars

Reference 27

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This paper cites Correct me if i’m wrong: Using non-experts to repair reinforcement learning policies.

A Survey On Enhancing Reinforcement Learning in Complex Environments: Insights from Human and LLM Feedback Correct me if i’m wrong: Using non-experts to repair reinforcement learning policies

Reference 28

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This paper cites Natural language specification of reinforcement learning policies through differentiable decision trees.

A Survey On Enhancing Reinforcement Learning in Complex Environments: Insights from Human and LLM Feedback Natural language specification of reinforcement learning policies through differentiable decision trees

Reference 29

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A Survey On Enhancing Reinforcement Learning in Complex Environments: Insights from Human and LLM Feedback Ella: Exploration through learned language abstraction

Reference 30

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This paper cites How to talk so ai will learn: Instructions, descriptions, and autonomy.

A Survey On Enhancing Reinforcement Learning in Complex Environments: Insights from Human and LLM Feedback How to talk so ai will learn: Instructions, descriptions, and autonomy

Reference 31

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A Survey On Enhancing Reinforcement Learning in Complex Environments: Insights from Human and LLM Feedback Human-in-the-loop reinforcement learning in continuous-action space

Reference 32

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A Survey On Enhancing Reinforcement Learning in Complex Environments: Insights from Human and LLM Feedback Toward human-in-the-loop ai: Enhancing deep reinforcement learning via real-time human guidance for autonomous driving

Reference 33

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A Survey On Enhancing Reinforcement Learning in Complex Environments: Insights from Human and LLM Feedback Deep reinforcement learning with interactive feedback in a human–robot environment

Reference 34

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A Survey On Enhancing Reinforcement Learning in Complex Environments: Insights from Human and LLM Feedback Deploying Offline Reinforcement Learning with Human Feedback

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A Survey On Enhancing Reinforcement Learning in Complex Environments: Insights from Human and LLM Feedback Widening the Pipeline in Human-Guided Reinforcement Learning with Explanation and Context-Aware Data Augmentation

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A Survey On Enhancing Reinforcement Learning in Complex Environments: Insights from Human and LLM Feedback The Expertise Problem: Learning from Specialized Feedback

Reference 37

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A Survey On Enhancing Reinforcement Learning in Complex Environments: Insights from Human and LLM Feedback Interactive reinforcement learning with bayesian fusion of multimodal advice

Reference 38

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A Survey On Enhancing Reinforcement Learning in Complex Environments: Insights from Human and LLM Feedback Policy shaping: Integrating human feedback with reinforcement learning

Reference 39

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A Survey On Enhancing Reinforcement Learning in Complex Environments: Insights from Human and LLM Feedback Learning from unreliable human action advice in interactive reinforcement learning

Reference 40

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A Survey On Enhancing Reinforcement Learning in Complex Environments: Insights from Human and LLM Feedback Improving deep reinforcement learning in minecraft with action advice

Reference 41

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A Survey On Enhancing Reinforcement Learning in Complex Environments: Insights from Human and LLM Feedback Advice-guided reinforce- ment learning in a non-markovian environment

Reference 42

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A Survey On Enhancing Reinforcement Learning in Complex Environments: Insights from Human and LLM Feedback Few-shot preference learning for human-in-the-loop rl

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:30:10.721309Z

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This paper cites Effect of human guidance and state space size on interactive reinforcement learning.

A Survey On Enhancing Reinforcement Learning in Complex Environments: Insights from Human and LLM Feedback Effect of human guidance and state space size on interactive reinforcement learning

Reference 44

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This paper cites Interactive reinforcement learning from demon- stration and human evaluative feedback.

A Survey On Enhancing Reinforcement Learning in Complex Environments: Insights from Human and LLM Feedback Interactive reinforcement learning from demon- stration and human evaluative feedback

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:30:10.686721Z

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Observation 21e5c316-b4eb-46b5-90db-7bfbba879e22 · outbound

This paper cites HAIM-DRL: Enhanced Human-in-the-loop Reinforcement Learning for Safe and Efficient Autonomous Driving.

A Survey On Enhancing Reinforcement Learning in Complex Environments: Insights from Human and LLM Feedback HAIM-DRL: Enhanced Human-in-the-loop Reinforcement Learning for Safe and Efficient Autonomous Driving

Reference 46

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Observation 2957aa46-575b-4285-b102-bf2f97ef7607 · outbound

This paper cites Tag: Teacher-advice mechanism with gaussian process for reinforcement learning.

A Survey On Enhancing Reinforcement Learning in Complex Environments: Insights from Human and LLM Feedback Tag: Teacher-advice mechanism with gaussian process for reinforcement learning

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:30:10.669002Z

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Observation 8a5396a2-821d-41da-9f83-8e1d8d724ee5 · outbound

This paper cites Guiding Pretraining in Reinforcement Learning with Large Language Models.

A Survey On Enhancing Reinforcement Learning in Complex Environments: Insights from Human and LLM Feedback Guiding Pretraining in Reinforcement Learning with Large Language Models

Reference 48

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This paper cites Reinforcement Learning from LLM Feedback to Counteract Goal Misgeneralization.

A Survey On Enhancing Reinforcement Learning in Complex Environments: Insights from Human and LLM Feedback Reinforcement Learning from LLM Feedback to Counteract Goal Misgeneralization

Reference 49

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This paper cites Mutual Enhancement of Large Language and Reinforcement Learning Models through Bi-Directional Feedback Mechanisms: A Planning Case Study.

A Survey On Enhancing Reinforcement Learning in Complex Environments: Insights from Human and LLM Feedback Mutual Enhancement of Large Language and Reinforcement Learning Models through Bi-Directional Feedback Mechanisms: A Planning Case Study

Reference 50

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A Survey On Enhancing Reinforcement Learning in Complex Environments: Insights from Human and LLM Feedback LaGR-SEQ: Language-Guided Reinforcement Learning with Sample-Efficient Querying

Reference 51

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A Survey On Enhancing Reinforcement Learning in Complex Environments: Insights from Human and LLM Feedback Reward Design with Language Models

Reference 52

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A Survey On Enhancing Reinforcement Learning in Complex Environments: Insights from Human and LLM Feedback Enabling Intelligent Interactions between an Agent and an LLM: A Reinforcement Learning Approach

Reference 53

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A Survey On Enhancing Reinforcement Learning in Complex Environments: Insights from Human and LLM Feedback Instruction-Following Agents with Multimodal Transformer

Reference 54

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A Survey On Enhancing Reinforcement Learning in Complex Environments: Insights from Human and LLM Feedback Eureka: Human-Level Reward Design via Coding Large Language Models

Reference 55

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A Survey On Enhancing Reinforcement Learning in Complex Environments: Insights from Human and LLM Feedback Inherently explainable reinforcement learning in natural language

Reference 56

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A Survey On Enhancing Reinforcement Learning in Complex Environments: Insights from Human and LLM Feedback Multi-granularity Knowledge Transfer for Continual Reinforcement Learning

Reference 57

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A Survey On Enhancing Reinforcement Learning in Complex Environments: Insights from Human and LLM Feedback Motif: Intrinsic Motivation from Artificial Intelligence Feedback

Reference 58

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A Survey On Enhancing Reinforcement Learning in Complex Environments: Insights from Human and LLM Feedback Building Open-Ended Embodied Agent via Language-Policy Bidirectional Adaptation

Reference 59

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A Survey On Enhancing Reinforcement Learning in Complex Environments: Insights from Human and LLM Feedback LLM Augmented Hierarchical Agents

Reference 60

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A Survey On Enhancing Reinforcement Learning in Complex Environments: Insights from Human and LLM Feedback Exploiting Contextual Structure to Generate Useful Auxiliary Tasks

Reference 61

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A Survey On Enhancing Reinforcement Learning in Complex Environments: Insights from Human and LLM Feedback WorldCoder, a Model-Based LLM Agent: Building World Models by Writing Code and Interacting with the Environment

Reference 62

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A Survey On Enhancing Reinforcement Learning in Complex Environments: Insights from Human and LLM Feedback Natural Language Reinforcement Learning

Reference 63

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A Survey On Enhancing Reinforcement Learning in Complex Environments: Insights from Human and LLM Feedback LLM-Grounder: Open-Vocabulary 3D Visual Grounding with Large Language Model as an Agent

Reference 64

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A Survey On Enhancing Reinforcement Learning in Complex Environments: Insights from Human and LLM Feedback Interactive planning using large language models for partially observable robotics tasks

Reference 65

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A Survey On Enhancing Reinforcement Learning in Complex Environments: Insights from Human and LLM Feedback Grounding language to entities and dynamics for generalization in reinforcement learning

Reference 66

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A Survey On Enhancing Reinforcement Learning in Complex Environments: Insights from Human and LLM Feedback An End-to-End Approach to Natural Language Object Retrieval via Context-Aware Deep Reinforcement Learning

Reference 67

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A Survey On Enhancing Reinforcement Learning in Complex Environments: Insights from Human and LLM Feedback Read, watch, and move: Reinforce- ment learning for temporally grounding natural language descriptions in videos

Reference 68

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A Survey On Enhancing Reinforcement Learning in Complex Environments: Insights from Human and LLM Feedback FollowNet: Robot Navigation by Following Natural Language Directions with Deep Reinforcement Learning

Reference 69

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A Survey On Enhancing Reinforcement Learning in Complex Environments: Insights from Human and LLM Feedback Toward collaborative reinforcement learning agents that commu- nicate through text-based natural language

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:30:10.588539Z

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A Survey On Enhancing Reinforcement Learning in Complex Environments: Insights from Human and LLM Feedback Language Understanding for Text-based Games Using Deep Reinforcement Learning

Reference 71

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A Survey On Enhancing Reinforcement Learning in Complex Environments: Insights from Human and LLM Feedback Towards End-to-End Learning for Dialog State Tracking and Management using Deep Reinforcement Learning

Reference 72

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A Survey On Enhancing Reinforcement Learning in Complex Environments: Insights from Human and LLM Feedback Online learning of task-driven object-based visual attention control

Reference 73

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A Survey On Enhancing Reinforcement Learning in Complex Environments: Insights from Human and LLM Feedback Visual navigation with spatial attention

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:30:10.553675Z

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This paper cites An Actor-Critic-Attention Mechanism for Deep Reinforcement Learning in Multi-view Environments.

A Survey On Enhancing Reinforcement Learning in Complex Environments: Insights from Human and LLM Feedback An Actor-Critic-Attention Mechanism for Deep Reinforcement Learning in Multi-view Environments

Reference 75

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A Survey On Enhancing Reinforcement Learning in Complex Environments: Insights from Human and LLM Feedback Deep reinforcement learning for the dynamic and uncertain vehicle routing problem

Reference 76

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A Survey On Enhancing Reinforcement Learning in Complex Environments: Insights from Human and LLM Feedback Optimizing attention for sequence modeling via reinforcement learning

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:30:10.519876Z

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A Survey On Enhancing Reinforcement Learning in Complex Environments: Insights from Human and LLM Feedback Attention-based curiosity-driven exploration in deep reinforcement learning

Reference 78

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A Survey On Enhancing Reinforcement Learning in Complex Environments: Insights from Human and LLM Feedback Towards interpretable reinforcement learning using attention augmented agents

Reference 79

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A Survey On Enhancing Reinforcement Learning in Complex Environments: Insights from Human and LLM Feedback Dynamic interaction between reinforcement learning and attention in multidimensional environments

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:30:10.472088Z

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A Survey On Enhancing Reinforcement Learning in Complex Environments: Insights from Human and LLM Feedback Machine versus Human Attention in Deep Reinforcement Learning Tasks

Reference 81

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This paper cites Rt-2: Vision-language-action models transfer web knowledge to robotic control.

A Survey On Enhancing Reinforcement Learning in Complex Environments: Insights from Human and LLM Feedback Rt-2: Vision-language-action models transfer web knowledge to robotic control

Reference 82

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A Survey On Enhancing Reinforcement Learning in Complex Environments: Insights from Human and LLM Feedback MLANet: Multi-Level Attention Network with Sub-instruction for Continuous Vision-and-Language Navigation

Reference 83

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Large Language Model Agent: A Survey on Methodology, Applications and Challenges cites this paper.

Large Language Model Agent: A Survey on Methodology, Applications and Challenges A Survey On Enhancing Reinforcement Learning in Complex Environments: Insights from Human and LLM Feedback

Reference 112

Resolution: verified exact; arxiv_id, observed 2026-05-22T21:52:10.206920Z

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MARCO: Meta-Reflection with Cross-Referencing for Code Reasoning cites this paper.

MARCO: Meta-Reflection with Cross-Referencing for Code Reasoning A Survey On Enhancing Reinforcement Learning in Complex Environments: Insights from Human and LLM Feedback

Reference 37

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LLM-Guided Reinforcement Learning: Addressing Training Bottlenecks through Policy Modulation A Survey On Enhancing Reinforcement Learning in Complex Environments: Insights from Human and LLM Feedback

Reference 14

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Evaluation of LLMs for mathematical problem solving A Survey On Enhancing Reinforcement Learning in Complex Environments: Insights from Human and LLM Feedback

Reference 27

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