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Active Reward Learning
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/11552246_35openalex-snapshot
Autonomous Inverted Helicopter Flight via Reinforcement Learning
inferredcites →https://doi.org/10.1007/978-3-540-28650-9_4openalex-snapshot
Gaussian Processes in Machine Learning
inferredcites →https://doi.org/10.1007/978-3-642-04174-7_3openalex-snapshot
Active Learning for Reward Estimation in Inverse Reinforcement Learning
inferredcites →https://doi.org/10.1007/978-3-642-23780-5_11openalex-snapshot
Preference-Based Policy Learning
inferredcites →https://doi.org/10.1007/978-3-642-23780-5_30openalex-snapshot
Preference-Based Policy Iteration: Leveraging Preference Learning for Reinforcement Learning
inferredcites →https://doi.org/10.1007/s10514-009-9121-3openalex-snapshot
Learning to search: Functional gradient techniques for imitation learning
inferredcites →https://doi.org/10.1016/0004-3702(94)90047-7openalex-snapshot
Robot shaping: developing autonomous agents through learning
inferredcites →https://doi.org/10.1016/j.artint.2007.09.009openalex-snapshot
Teachable robots: Understanding human teaching behavior to build more effective robot learners
inferredcites →https://doi.org/10.1016/j.robot.2010.06.001openalex-snapshot
Combining active learning and reactive control for robot grasping
inferredcites →https://doi.org/10.1037//0033-295x.101.2.343openalex-snapshot
The magical number seven, plus or minus two: Some limits on our capacity for processing information.
inferredcites →https://doi.org/10.1037/0033-295x.101.2.343openalex-snapshot
The magical number seven, plus or minus two: Some limits on our capacity for processing information.
inferredcites →https://doi.org/10.1037/h0055485openalex-snapshot
The effect of presenting various numbers of discrete steps on scale reading accuracy.
inferredcites →https://doi.org/10.1109/icra.2013.6630937openalex-snapshot
Learning sequential motor tasks
inferredcites →https://doi.org/10.1109/iros.2008.4650953openalex-snapshot
Learning perceptual coupling for motor primitives
inferredcites →https://doi.org/10.1109/iros.2010.5649089openalex-snapshot
Robot motor skill coordination with EM-based Reinforcement Learning
inferredcites →https://doi.org/10.1109/tamd.2010.2051031openalex-snapshot
Intrinsically Motivated Reinforcement Learning: An Evolutionary Perspective
inferredcites →https://doi.org/10.1109/tit.2011.2182033openalex-snapshot
Information-Theoretic Regret Bounds for Gaussian Process Optimization in the Bandit Setting
inferredcites →https://doi.org/10.1109/tro.2012.2189498openalex-snapshot
Physical Human Interactive Guidance: Identifying Grasping Principles From Human-Planned Grasps
inferredcites →https://doi.org/10.1145/1102351.1102369openalex-snapshot
Preference learning with Gaussian processes
inferredcites →https://doi.org/10.1145/1102351.1102377openalex-snapshot
Reinforcement learning with Gaussian processes
inferredcites →https://doi.org/10.1145/1597735.1597738openalex-snapshot
Interactively shaping agents via human reinforcement
inferredcites →https://doi.org/10.1145/2157689.2157693openalex-snapshot
Designing robot learners that ask good questions
inferredcites →https://doi.org/10.1184/r1/6555512openalex-snapshot
Maximum Entropy Inverse Reinforcement Learning
inferredcites →https://doi.org/10.1609/aaai.v24i1.7727openalex-snapshot
Relative Entropy Policy Search
inferredcites →https://doi.org/10.48550/arxiv.1009.5419openalex-snapshot
Portfolio Allocation for Bayesian Optimization
inferredcites →https://doi.org/10.48550/arxiv.1306.6294openalex-snapshot
Learning Trajectory Preferences for Manipulators via Iterative Improvement
inferredcites →https://doi.org/10.5555/2343576.2343634openalex-snapshot
Strong mitigation: nesting search for good policies within search for good reward
inferredcites →https://doi.org/10.65109/jjtt8551openalex-snapshot
Dynamic potential-based reward shaping
inferredcites →https://doi.org/10.7551/mitpress/7503.003.0062openalex-snapshot
Bayesian Policy Gradient Algorithms
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Human in the AI Loop via xAI and Active Learning for Visual Inspection
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aphBO-2GP-3B: a budgeted asynchronous parallel multi-acquisition functions for constrained Bayesian optimization on high-performing computing architecture
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An asynchronous parallel high-throughput model calibration framework for crystal plasticity finite element constitutive models
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Real-time bidding with multi-agent reinforcement learning in multi-channel display advertising
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Active learning via informed search in movement parameter space for efficient robot task learning and transfer
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Towards Bidirectional and Coadaptive Robotic Exoskeletons for Neuromotor Rehabilitation and Assisted Daily Living: a Review
inferred← citeshttps://doi.org/10.1016/j.csl.2018.02.003openalex-snapshot
Reward estimation for dialogue policy optimisation
inferred← citeshttps://doi.org/10.1016/j.robot.2018.11.004openalex-snapshot
Deep reinforcement learning with smooth policy update: Application to robotic cloth manipulation
inferred← citeshttps://doi.org/10.1038/s41467-025-66009-yopenalex-snapshot
Discovery of the reward function for embodied reinforcement learning agents
inferred← citeshttps://doi.org/10.1038/s41598-017-17682-7openalex-snapshot
Intrinsic interactive reinforcement learning – Using error-related potentials for real world human-robot interaction
inferred← citeshttps://doi.org/10.1038/s41598-022-05784-wopenalex-snapshot
Sample-efficient parameter exploration of the powder film drying process using experiment-based Bayesian optimization
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Alignment for Advanced Machine Learning Systems
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Multi-domain dialogue success classifiers for policy training
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Learning Human Ergonomic Preferences for Handovers
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Residual Reinforcement Learning for Robot Control
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Active Reward Learning for Co-Robotic Vision Based Exploration in Bandwidth Limited Environments
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Flexible online adaptation of learning strategy using EEG-based reinforcement signals in real-world robotic applications
inferred← citeshttps://doi.org/10.1109/icra48891.2023.10160692openalex-snapshot
Efficient Inference of Temporal Task Specifications from Human Demonstrations using Experiment Design
inferred← citeshttps://doi.org/10.1109/ijcnn52387.2021.9534141openalex-snapshot
Neural Task Success Classifiers for Robotic Manipulation from Few Real Demonstrations
inferred← citeshttps://doi.org/10.1109/iros.2018.8594279openalex-snapshot
Towards Intelligent Arbitration of Diverse Active Learning Queries
inferred← citeshttps://doi.org/10.1109/iros45743.2020.9340709openalex-snapshot
Contextual Policy Search for Micro-Data Robot Motion Learning through Covariate Gaussian Process Latent Variable Models
inferred← citeshttps://doi.org/10.1109/iros45743.2020.9341530openalex-snapshot
Active Preference Learning using Maximum Regret
inferred← citeshttps://doi.org/10.1109/iros45743.2020.9341714openalex-snapshot
Deep Reinforcement Learning for Industrial Insertion Tasks with Visual Inputs and Natural Rewards
inferred← citeshttps://doi.org/10.1109/iros45743.2020.9341803openalex-snapshot
Learning of Tool Force Adjustment Skills by a Life-sized Humanoid using Deep Reinforcement Learning and Active Teaching Request
inferred← citeshttps://doi.org/10.1109/iros51168.2021.9636184openalex-snapshot
Pairwise Preferences-Based Optimization of a Path-Based Velocity Planner in Robotic Sealing Tasks
inferred← citeshttps://doi.org/10.1109/iros51168.2021.9636515openalex-snapshot
Meta Preference Learning for Fast User Adaptation in Human-Supervisory Multi-Robot Deployments
inferred← citeshttps://doi.org/10.1109/lra.2019.2897342openalex-snapshot
Bayesian Active Learning for Collaborative Task Specification Using Equivalence Regions
inferred← citeshttps://doi.org/10.1109/lra.2021.3063927openalex-snapshot
Human-Guided Robot Behavior Learning: A GAN-Assisted Preference-Based Reinforcement Learning Approach
inferred← citeshttps://doi.org/10.1109/lra.2021.3094479openalex-snapshot
Pairwise Preferences-Based Optimization of a Path-Based Velocity Planner in Robotic Sealing Tasks
inferred← citeshttps://doi.org/10.1109/southeastcon44009.2020.9249705openalex-snapshot
Measuring driving performance for an All-Terrain Vehicle in a paved road in the woods
inferred← citeshttps://doi.org/10.1109/tcst.2023.3279949openalex-snapshot
Active Preference Learning for Vehicle Suspensions Calibration
inferred← citeshttps://doi.org/10.1109/tro.2023.3324580openalex-snapshot
Optimization-Based Control for Dynamic Legged Robots
inferred← citeshttps://doi.org/10.1109/tsg.2022.3154718openalex-snapshot
Reinforcement Learning for Selective Key Applications in Power Systems: Recent Advances and Future Challenges
inferred← citeshttps://doi.org/10.1109/wacv45572.2020.9093406openalex-snapshot
Preference-Based Image Generation
inferred← citeshttps://doi.org/10.1115/1.4046697openalex-snapshot
sMF-BO-2CoGP: A Sequential Multi-Fidelity Constrained Bayesian Optimization Framework for Design Applications
inferred← citeshttps://doi.org/10.1115/detc2021-70828openalex-snapshot
Scalable3-BO: Big Data Meets HPC - A Scalable Asynchronous Parallel High-Dimensional Bayesian Optimization Framework on Supercomputers
inferred← citeshttps://doi.org/10.1145/3171221.3171284openalex-snapshot
Learning from Richer Human Guidance
inferred← citeshttps://doi.org/10.1145/3306618.3314250openalex-snapshot
Incomplete Contracting and AI Alignment
inferred← citeshttps://doi.org/10.1145/3526107openalex-snapshot
Here’s What I’ve Learned: Asking Questions that Reveal Reward Learning
inferred← citeshttps://doi.org/10.1177/02783649241273901openalex-snapshot
Robot learning on the job: Human-in-the-loop autonomy and learning during deployment
inferred← citeshttps://doi.org/10.15607/rss.2017.xiii.053openalex-snapshot
Active Preference-Based Learning of Reward Functions
inferred← citeshttps://doi.org/10.15607/rss.2019.xv.020openalex-snapshot
Unsupervised Visuomotor Control through Distributional Planning Networks
inferred← citeshttps://doi.org/10.15607/rss.2019.xv.073openalex-snapshot
End-To-End Robotic Reinforcement Learning without Reward Engineering
inferred← citeshttps://doi.org/10.15607/rss.2020.xvi.024openalex-snapshot
AVID: Learning Multi-Stage Tasks via Pixel-Level Translation of Human Videos
inferred← citeshttps://doi.org/10.15607/rss.2023.xix.005openalex-snapshot
Robot Learning on the Job: Human-in-the-Loop Autonomy and Learning During Deployment
inferred← citeshttps://doi.org/10.1575/1912/25831openalex-snapshot
Enabling human-robot cooperation in scientific exploration of bandwidth-limited environments
inferred← citeshttps://doi.org/10.1609/aaai.v30i1.10411openalex-snapshot
Distance Minimization for Reward Learning from Scored Trajectories
inferred← citeshttps://doi.org/10.1609/aaai.v32i1.11490openalex-snapshot
Toward Deep Reinforcement Learning Without a Simulator: An Autonomous Steering Example
inferred← citeshttps://doi.org/10.1609/aaai.v34i03.5630openalex-snapshot
What Is It You Really Want of Me? Generalized Reward Learning with Biased Beliefs about Domain Dynamics
inferred← citeshttps://doi.org/10.1609/aaai.v38i15.29666openalex-snapshot
Decoding Global Preferences: Temporal and Cooperative Dependency Modeling in Multi-Agent Preference-Based Reinforcement Learning
inferred← citeshttps://doi.org/10.18653/v1/d19-6407openalex-snapshot
Learning to request guidance in emergent language
inferred← citeshttps://doi.org/10.18653/v1/p16-1230openalex-snapshot
On-line Active Reward Learning for Policy Optimisation in Spoken Dialogue Systems
inferred← citeshttps://doi.org/10.21437/interspeech.2015-456openalex-snapshot
Learning from real users: rating dialogue success with neural networks for reinforcement learning in spoken dialogue systems
inferred← citeshttps://doi.org/10.23919/acc63710.2025.11107834openalex-snapshot
Active Inverse Learning in Stackelberg Trajectory Games
inferred← citeshttps://doi.org/10.24963/ijcai.2019/283openalex-snapshot
Active Learning within Constrained Environments through Imitation of an Expert Questioner
inferred← citeshttps://doi.org/10.24963/ijcai.2021/599openalex-snapshot
Understanding the Relationship between Interactions and Outcomes in Human-in-the-Loop Machine Learning
inferred← citeshttps://doi.org/10.3389/fnbot.2023.1297990openalex-snapshot
Detection of tactile-based error-related potentials (ErrPs) in human-robot interaction
inferred← citeshttps://doi.org/10.3389/frobt.2020.558531openalex-snapshot
Errors in Human-Robot Interactions and Their Effects on Robot Learning
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Deep Reinforcement Learning for Real-World Humanoid Robot Locomotion Control with Automatic Reward Learning
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Agnostic Learning with Unknown Utilities
inferred← citeshttps://doi.org/10.48550/arxiv.1603.06390openalex-snapshot
Learning Dynamic Robot-to-Human Object Handover from Human Feedback
inferred← citeshttps://doi.org/10.48550/arxiv.1612.01086openalex-snapshot
Deep Learning of Robotic Tasks without a Simulator using Strong and Weak Human Supervision
inferred← citeshttps://doi.org/10.48550/arxiv.1701.04079openalex-snapshot
Agent-Agnostic Human-in-the-Loop Reinforcement Learning
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Deep reinforcement learning from human preferences
inferred← citeshttps://doi.org/10.48550/arxiv.1802.01604openalex-snapshot
Learning from Richer Human Guidance: Augmenting Comparison-Based Learning with Feature Queries
inferred← citeshttps://doi.org/10.48550/arxiv.1803.04926openalex-snapshot
Active Reinforcement Learning with Monte-Carlo Tree Search
inferred← citeshttps://doi.org/10.48550/arxiv.1809.03060openalex-snapshot
Active Inverse Reward Design
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Asking Easy Questions: A User-Friendly Approach to Active Reward Learning
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Learning to Request Guidance in Emergent Communication
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Learning Human Objectives by Evaluating Hypothetical Behavior
inferred← citeshttps://doi.org/10.48550/arxiv.2008.13221openalex-snapshot
Human-in-the-Loop Methods for Data-Driven and Reinforcement Learning Systems
inferred← citeshttps://doi.org/10.48550/arxiv.2011.06709openalex-snapshot
Active Reinforcement Learning: Observing Rewards at a Cost
inferred← citeshttps://doi.org/10.48550/arxiv.2011.09999openalex-snapshot
Inverse Constrained Reinforcement Learning
inferred← citeshttps://doi.org/10.48550/arxiv.2103.12142openalex-snapshot
Combining Reward Information from Multiple Sources
inferred← citeshttps://doi.org/10.48550/arxiv.2106.05091openalex-snapshot
PEBBLE: Feedback-Efficient Interactive Reinforcement Learning via Relabeling Experience and Unsupervised Pre-training
inferred← citeshttps://doi.org/10.48550/arxiv.2106.10251openalex-snapshot
Active Offline Policy Selection
inferred← citeshttps://doi.org/10.48550/arxiv.2107.01995openalex-snapshot
Here's What I've Learned: Asking Questions that Reveal Reward Learning
inferred← citeshttps://doi.org/10.48550/arxiv.2111.09884openalex-snapshot
Assisted Robust Reward Design
inferred← citeshttps://doi.org/10.5445/ir/1000118270openalex-snapshot
A survey of preference-based reinforcement learning methods
inferred← citeshttps://doi.org/10.5555/2936924.2936990openalex-snapshot
Exploration from Demonstration for Interactive Reinforcement Learning
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