Learning from Active Human Involvement through Proxy Value Propagation | Paper Citation Record

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This paper cites Agent-Agnostic Human-in-the-Loop Reinforcement Learning.

Learning from Active Human Involvement through Proxy Value Propagation Agent-Agnostic Human-in-the-Loop Reinforcement Learning

Reference 1

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Observation 1f779148-5b50-4f46-ab15-ea9f0849d2da · outbound

This paper cites Constrained policy optimization.

Learning from Active Human Involvement through Proxy Value Propagation Constrained policy optimization

Reference 2

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Observation 6551bf87-7e4c-4241-9215-45c03fb950f1 · outbound

This paper cites An interactive framework for learning continuous actions policies based on corrective feedback.

Learning from Active Human Involvement through Proxy Value Propagation An interactive framework for learning continuous actions policies based on corrective feedback

Reference 3

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This paper cites Minimalistic gridworld environment for openai gym.

Learning from Active Human Involvement through Proxy Value Propagation Minimalistic gridworld environment for openai gym

Reference 4

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This paper cites Christiano, Jan Leike, Tom B.

Learning from Active Human Involvement through Proxy Value Propagation Christiano, Jan Leike, Tom B

Reference 5

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This paper cites Open Problems in Cooperative AI.

Learning from Active Human Involvement through Proxy Value Propagation Open Problems in Cooperative AI

Reference 6

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This paper cites Magnetic control of tokamak plasmas through deep reinforcement learning.

Learning from Active Human Involvement through Proxy Value Propagation Magnetic control of tokamak plasmas through deep reinforcement learning

Reference 7

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Observation 5cbdc2e8-a9e3-4a6e-92db-05407621c93c · outbound

This paper cites CARLA: An open urban driving simulator.

Learning from Active Human Involvement through Proxy Value Propagation CARLA: An open urban driving simulator

Reference 8

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Observation e5614c5d-7d61-4601-9830-9e1c162b2a6f · outbound

This paper cites Learning robust rewards with adverserial inverse reinforcement learning.

Learning from Active Human Involvement through Proxy Value Propagation Learning robust rewards with adverserial inverse reinforcement learning

Reference 9

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Observation 7b39b107-02c1-44ed-804f-de83d3e30c30 · outbound

This paper cites Addressing function approximation error in actor-critic methods.

Learning from Active Human Involvement through Proxy Value Propagation Addressing function approximation error in actor-critic methods

Reference 10

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Observation 7222c1b5-b6b1-4e9e-a42b-73c4e1fd9997 · outbound

This paper cites Widening the pipeline in human-guided reinforcement learning with explanation and context-aware data augmentation.

Learning from Active Human Involvement through Proxy Value Propagation Widening the pipeline in human-guided reinforcement learning with explanation and context-aware data augmentation

Reference 11

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Observation 8aac2172-d86a-40e0-b6c3-1092cba33595 · outbound

This paper cites Learning to walk in the real world with minimal human effort, 2020.

Learning from Active Human Involvement through Proxy Value Propagation Learning to walk in the real world with minimal human effort, 2020

Reference 12

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Observation 5814ac2b-f61b-4abc-a940-19b105a23d71 · outbound

This paper cites Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor.

Learning from Active Human Involvement through Proxy Value Propagation Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor

Reference 13

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Observation a12310a7-8404-448e-b892-16650f1dd590 · outbound

This paper cites Generative adversarial imitation learning.

Learning from Active Human Involvement through Proxy Value Propagation Generative adversarial imitation learning

Reference 14

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Observation 2d9f4182-d0a1-46b2-a062-953718f16410 · outbound

This paper cites Precise Synthetic Image and LiDAR (PreSIL) Dataset for Autonomous Vehicle Perception.

Learning from Active Human Involvement through Proxy Value Propagation Precise Synthetic Image and LiDAR (PreSIL) Dataset for Autonomous Vehicle Perception

Reference 15

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Observation 2d88299e-4df6-4c4a-8b8b-cf1c3d882959 · outbound

This paper cites Learning to share autonomy across repeated interaction.

Learning from Active Human Involvement through Proxy Value Propagation Learning to share autonomy across repeated interaction

Reference 16

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This paper cites Hg-dagger: Interactive imitation learning with human experts.

Learning from Active Human Involvement through Proxy Value Propagation Hg-dagger: Interactive imitation learning with human experts

Reference 17

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This paper cites Learning to drive in a day.

Learning from Active Human Involvement through Proxy Value Propagation Learning to drive in a day

Reference 18

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Observation 12e15a05-11a0-42cc-a893-0fdd3527d49e · outbound

This paper cites Reinforcement learning from human reward: Discounting in episodic tasks.

Learning from Active Human Involvement through Proxy Value Propagation Reinforcement learning from human reward: Discounting in episodic tasks

Reference 19

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Observation 8ec8c7b7-888c-4308-a845-7c7bf919bb68 · outbound

This paper cites Specification gaming: the flip side of ai ingenuity.

Learning from Active Human Involvement through Proxy Value Propagation Specification gaming: the flip side of ai ingenuity

Reference 20

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Observation eae9d699-d97b-4fa8-8a8f-b935d18a6caf · outbound

This paper cites Conservative q-learning for offline reinforcement learning.

Learning from Active Human Involvement through Proxy Value Propagation Conservative q-learning for offline reinforcement learning

Reference 21

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Observation 480fa63f-cede-40c0-b88d-6a38f8e293db · outbound

This paper cites PEBBLE: Feedback-Efficient Interactive Reinforcement Learning via Relabeling Experience and Unsupervised Pre-training.

Learning from Active Human Involvement through Proxy Value Propagation PEBBLE: Feedback-Efficient Interactive Reinforcement Learning via Relabeling Experience and Unsupervised Pre-training

Reference 22

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This paper cites Scalable agent alignment via reward modeling: a research direction.

Learning from Active Human Involvement through Proxy Value Propagation Scalable agent alignment via reward modeling: a research direction

Reference 23

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Observation 800cc48d-9186-41e3-bec0-9b126f665946 · outbound

This paper cites Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems.

Learning from Active Human Involvement through Proxy Value Propagation Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems

Reference 24

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This paper cites Metadrive: Composing diverse driving scenarios for generalizable reinforcement learning.

Learning from Active Human Involvement through Proxy Value Propagation Metadrive: Composing diverse driving scenarios for generalizable reinforcement learning

Reference 25

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This paper cites Efficient learning of safe driving policy via human-ai copilot optimization.

Learning from Active Human Involvement through Proxy Value Propagation Efficient learning of safe driving policy via human-ai copilot optimization

Reference 26

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This paper cites Interactive learning from policy-dependent human feedback.

Learning from Active Human Involvement through Proxy Value Propagation Interactive learning from policy-dependent human feedback

Reference 27

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation c17b998b-b5a3-4ca8-bef6-efdf4058b2bf · outbound

This paper cites Where to add actions in human-in- the-loop reinforcement learning.

Learning from Active Human Involvement through Proxy Value Propagation Where to add actions in human-in- the-loop reinforcement learning

Reference 28

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Observation 1010a489-38aa-4c5b-b1b4-c5e7c20ef80b · outbound

This paper cites Human-in-the-Loop Imitation Learning using Remote Teleoperation.

Learning from Active Human Involvement through Proxy Value Propagation Human-in-the-Loop Imitation Learning using Remote Teleoperation

Reference 29

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This paper cites Ensembledagger: A bayesian approach to safe imitation learning.

Learning from Active Human Involvement through Proxy Value Propagation Ensembledagger: A bayesian approach to safe imitation learning

Reference 30

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Observation ced1ce6b-ef94-4b41-b8bb-c876ef40c9b6 · outbound

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

Learning from Active Human Involvement through Proxy Value Propagation Human-level control through deep reinforcement learning

Reference 31

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This paper cites Interactively shaping robot behaviour with unlabeled human instructions.

Learning from Active Human Involvement through Proxy Value Propagation Interactively shaping robot behaviour with unlabeled human instructions

Reference 32

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This paper cites Deep exploration via bootstrapped DQN.

Learning from Active Human Involvement through Proxy Value Propagation Deep exploration via bootstrapped DQN

Reference 33

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This paper cites Training language models to follow instructions with human feedback.

Learning from Active Human Involvement through Proxy Value Propagation Training language models to follow instructions with human feedback

Reference 34

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This paper cites Deeptake: Prediction of driver takeover behavior using multimodal data.

Learning from Active Human Involvement through Proxy Value Propagation Deeptake: Prediction of driver takeover behavior using multimodal data

Reference 35

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Observation d77a5421-8af5-435e-a7f1-2d5400274dd5 · outbound

This paper cites Learning reward functions by integrating human demonstrations and preferences.

Learning from Active Human Involvement through Proxy Value Propagation Learning reward functions by integrating human demonstrations and preferences

Reference 36

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 3504d69c-1fdf-4a9a-b3b2-fb81bd3064b2 · outbound

This paper cites Stable-baselines3: Reliable reinforcement learning implementations.

Learning from Active Human Involvement through Proxy Value Propagation Stable-baselines3: Reliable reinforcement learning implementations

Reference 37

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Observation a4aa344c-5a33-4a04-b2c4-819b55859e9f · outbound

This paper cites Shared autonomy via deep reinforcement learning.

Learning from Active Human Involvement through Proxy Value Propagation Shared autonomy via deep reinforcement learning

Reference 38

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This paper cites Efficient reductions for imitation learning.

Learning from Active Human Involvement through Proxy Value Propagation Efficient reductions for imitation learning

Reference 39

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Observation 512fd1f9-fd5d-493b-8cf5-ce44b21155d6 · outbound

This paper cites Human compatible: Artificial intelligence and the problem of control.

Learning from Active Human Involvement through Proxy Value Propagation Human compatible: Artificial intelligence and the problem of control

Reference 40

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This paper cites Active preference-based learning of reward functions.

Learning from Active Human Involvement through Proxy Value Propagation Active preference-based learning of reward functions

Reference 41

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This paper cites The StarCraft Multi-Agent Challenge.

Learning from Active Human Involvement through Proxy Value Propagation The StarCraft Multi-Agent Challenge

Reference 42

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This paper cites Trial without error: Towards safe reinforcement learning via human intervention.

Learning from Active Human Involvement through Proxy Value Propagation Trial without error: Towards safe reinforcement learning via human intervention

Reference 43

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation a52999c7-3485-4783-b7a0-55bf2061e842 · outbound

This paper cites Prioritized Experience Replay.

Learning from Active Human Involvement through Proxy Value Propagation Prioritized Experience Replay

Reference 44

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This paper cites Proximal Policy Optimization Algorithms.

Learning from Active Human Involvement through Proxy Value Propagation Proximal Policy Optimization Algorithms

Reference 45

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

Learning from Active Human Involvement through Proxy Value Propagation Mastering the game of go with deep neural networks and tree search

Reference 46

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Observation 7fd6d2e5-f549-4dab-a89d-eb87b501cb25 · outbound

This paper cites Learning from interventions.

Learning from Active Human Involvement through Proxy Value Propagation Learning from interventions

Reference 47

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Observation c3b36b0b-6a65-4bec-bebb-593a2260a381 · outbound

This paper cites Responsive safety in reinforcement learning by PID lagrangian methods.

Learning from Active Human Involvement through Proxy Value Propagation Responsive safety in reinforcement learning by PID lagrangian methods

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T05:02:13.685480Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 16f063e0-5ee7-4fe0-a36f-97e2e770258a · outbound

This paper cites Intervention aided reinforcement learning for safe and practical policy optimization in navigation.

Learning from Active Human Involvement through Proxy Value Propagation Intervention aided reinforcement learning for safe and practical policy optimization in navigation

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T05:02:13.675802Z

Source-reported events for the cited work

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This paper cites Appli: Adaptive planner parameter learning from interventions.

Learning from Active Human Involvement through Proxy Value Propagation Appli: Adaptive planner parameter learning from interventions

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T05:02:13.665765Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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This paper cites Apple: Adaptive planner parameter learning from evaluative feedback.

Learning from Active Human Involvement through Proxy Value Propagation Apple: Adaptive planner parameter learning from evaluative feedback

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T05:02:13.655365Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation f6731b1a-369e-46d1-ab34-e27019e4ca7c · outbound

This paper cites Waytowich, Vernon Lawhern, and Peter Stone.

Learning from Active Human Involvement through Proxy Value Propagation Waytowich, Vernon Lawhern, and Peter Stone

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T05:02:13.644940Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 9efe55a1-6684-4c74-95cc-3541b6f622e2 · outbound

This paper cites A survey of preference-based reinforcement learning methods.

Learning from Active Human Involvement through Proxy Value Propagation A survey of preference-based reinforcement learning methods

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T05:02:13.635146Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation fff5f2f2-54f5-4012-8fb2-f5dd6ee30d87 · outbound

This paper cites Look before you leap: Safe model-based reinforcement learning with human intervention.

Learning from Active Human Involvement through Proxy Value Propagation Look before you leap: Safe model-based reinforcement learning with human intervention

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T05:02:13.624382Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 4a6b2247-a7b6-4c53-8a33-62eec05ac09a · outbound

This paper cites How to Leverage Unlabeled Data in Offline Reinforcement Learning.

Learning from Active Human Involvement through Proxy Value Propagation How to Leverage Unlabeled Data in Offline Reinforcement Learning

Reference 55

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Source-reported events for the cited work

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Observation 1cba8519-29cb-44e7-beb5-2e893ce4d370 · outbound

This paper cites Query-efficient imitation learning for end-to-end simulated driving.

Learning from Active Human Involvement through Proxy Value Propagation Query-efficient imitation learning for end-to-end simulated driving

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T05:02:13.613347Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation e950529c-f767-45f5-9422-d19e5ac28403 · outbound

This paper cites We also compare the behavior of agents learned from PVP and TD3 baseline.

Learning from Active Human Involvement through Proxy Value Propagation We also compare the behavior of agents learned from PVP and TD3 baseline

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T05:02:13.603389Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 6068c2d7-ed81-48b2-89b1-a6aefef1393f · outbound

This paper cites We present the behavior comparison between PVP and TD3 baseline.

Learning from Active Human Involvement through Proxy Value Propagation We present the behavior comparison between PVP and TD3 baseline

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T05:02:13.592962Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 9bf4e8f1-30c6-4542-af09-7e41c28670bf · outbound

This paper cites PVP performs well in GTA V and can drive smoothly on the highway.

Learning from Active Human Involvement through Proxy Value Propagation PVP performs well in GTA V and can drive smoothly on the highway

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T05:02:13.581248Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation c5bcf62e-6cee-4520-b026-ec406bf02213 · outbound

This paper cites If the agent drives in the wrong way then the displace- ment reward will be multiplied by −1.

Learning from Active Human Involvement through Proxy Value Propagation If the agent drives in the wrong way then the displace- ment reward will be multiplied by −1

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T05:02:13.570786Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation c52ab57e-f03e-4c80-a06c-8884e315b45d · outbound

This paper cites If the agent drives in wrong way then the speed reward will be multiplied by −1.

Learning from Active Human Involvement through Proxy Value Propagation If the agent drives in wrong way then the speed reward will be multiplied by −1

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T05:02:13.559970Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 17d20be8-c420-46e8-acdc-458c05267c24 · outbound

This paper cites Otherwise, it is 0.

Learning from Active Human Involvement through Proxy Value Propagation Otherwise, it is 0

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T05:02:13.549236Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation f0fc79af-f7b3-490a-81a4-4890185e7071 · outbound

This paper cites At that step, we set Rdisp = Rspeed = Rcollision = 0and assign Rterm according to the terminal state.

Learning from Active Human Involvement through Proxy Value Propagation At that step, we set Rdisp = Rspeed = Rcollision = 0and assign Rterm according to the terminal state

Reference 63

Resolution: malformed identifier; raw_fallback, observed 2026-08-09T05:02:13.537874Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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