Overcoming Exploration in Reinforcement Learning with Demonstrations

Adam: A Method for Stochastic Optimization

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Human-level control through deep reinforcement learning

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Neural Machine Translation by Jointly Learning to Align and Translate

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Mastering the game of Go with deep neural networks and tree search

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Asynchronous Methods for Deep Reinforcement Learning

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MuJoCo: A physics engine for model-based control

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Trust region policy optimization

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End to End Learning for Self-Driving Cars

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TendencyRL: Multi-stage Discriminative Hints for Efficient Goal-Oriented Reverse Curriculum Learning

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Transferring Domain Knowledge with an Adviser in Continuous Tasks

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Industrial Human-Robot Collaboration

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Supervised Reinforcement Learning via Value Function

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Highway Pilot Training from Demonstration

Pankiewicz, N., Turlej, W., Orłowski, M., & Wrona, T. (2021) · International Conference on Methods and Models in Automation and Robotics · doi:10.1109/mmar49549.2021.9528436 · Cited by 2

Offline reinforcement learning with anderson acceleration for robotic tasks

Zuo, G., Huang, S., Li, J., & Gong, D. (2022) · Applied Intelligence · doi:10.1007/s10489-021-02953-8 · Cited by 2

Multi-goal Reinforcement Learning via Exploring Successor Matching

Feng, X. (2022) · IEEE Conference on Games (CoG) · doi:10.1109/cog51982.2022.9893637 · Cited by 2

A Method for High-Value Driving Demonstration Data Generation Based on One-Dimensional Deep Convolutional Generative Adversarial Networks

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Guided Deep Reinforcement Learning based on RBF-ARX Pseudo LQR in Single Stage Inverted Pendulum

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Tutorial on Course-of-Action (COA) Attack Search Methods in Computer Networks

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Empowerment-driven Policy Gradient Learning with Counterfactual Augmentation in Recommender Systems

Chen, X., Yao, L., Chang, X., & Wang, S. (2022) · Proceedings · doi:10.1109/icdm54844.2022.00102 · Cited by 2

Trajectory Generation for Space Manipulators Capturing Moving Targets Using Transfer Learning

Sze, H. Y., & Chhabra, R. (2023) · IEEE Aerospace Conference (AERO) · doi:10.1109/aero55745.2023.10115862 · Cited by 2

Robotic Stacking of Irregular Objects with Load Position Identification and Compensation

Zhou, X., Zhao, G., Xing, Y., Wu, J., & Xiong, Z. (2023) · IEEE International Conference on Automation Science and Engineering (CASE) · doi:10.1109/case56687.2023.10260654 · Cited by 2

Aiding reinforcement learning for set point control

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Embedding expert demonstrations into clustering buffer for effective deep reinforcement learning

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Guiding Offline Reinforcement Learning Using a Safety Expert

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Learning Complicated Manipulation Skills Via Deterministic Policy with Limited Demonstrations

Liu, H., Tan, J., Chen, Y., & Ang, M. H. (2023) · International Conference on Advanced Robotics (ICAR) · doi:10.1109/icar58858.2023.10406318 · Cited by 3

Reinforcement Learning-Based Skill Acquisition Study for Linearly Heated Robots

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Adaptive algorithms for shaping behavior

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A Comparative Study of Model-Free Reinforcement Learning Approaches

Moudgalya, A., Shafi, A., & Arun, B. A. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-981-15-3383-9_50 · Cited by 2

Reinforcement Learning with Converging Goal Space and Binary Reward Function

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Augmenting Policy Learning with Routines Discovered from a Single Demonstration

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Packet Drop Probability-Optimal Cross-layer Scheduling: Dealing with Curse of Sparsity using Prioritized Experience Replay

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Pretrain Soft Q-Learning with Imperfect DemonstrationsPreprint

Zhang, X., Li, Y., Ma, H., & Luo, X. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.03501 · Cited by 3

Evolutionary Selective Imitation: Interpretable Agents by Imitation Learning Without a DemonstratorPreprint

Eliya, R., & Herrmann, J. M. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2009.08403 · Cited by 2

Prioritized Experience-based Reinforcement Learning with Human Guidance: Methdology and Application to Autonomous Driving.Preprint

Wu, J., Huang, Z., Huang, W., & Lv, C. (2021) · arXiv (Cornell University) · Cited by 2

Improving Learning from Demonstrations by Learning from ExperiencePreprint

Liu, H., Chen, Y., Tan, J., & Ang, M. H. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2111.08156 · Cited by 3

On Simple Reactive Neural Networks for Behaviour-Based Reinforcement Learning

Pore, A., & Aragón-Camarasa, G. (2020) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra40945.2020.9197262 · Cited by 15

Accelerating Reinforcement Learning with Suboptimal Guidance

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Smart Start and HER for a Directed and Persistent Reinforcement Learning Exploration in Discrete Environment

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Trajectory-based Split Hindsight Reverse Curriculum Learning

Wu, J., Zhang, D., Zhong, S., & Qiao, H. (2021) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros51168.2021.9636842 · Cited by 1

Visual Object Manipulation Based on Exploration Guided by Demonstration

Kim, D.-J., Jo, H., & Song, J.-B. (2022) · The Journal of Korea Robotics Society · doi:10.7746/jkros.2022.17.1.040 · Cited by 1

Learning Deposition Policies for Fused Multi-Material 3D Printing

Liao, K., Tricard, T., Piovarči, M., Seidel, H., & Babaei, V. (2023) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48891.2023.10160465 · Cited by 2

Learning Flight Control Systems from Human Demonstrations and Real-Time Uncertainty-Informed Interventions

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Exploring demonstration pre-training with improved Deep Q-learning

Pettersson, M., Westphal, F., & Riveiro, M. (2024) · Linköping electronic conference proceedings · doi:10.3384/ecp208008 · Cited by 1

Deep Reinforcement Learning for Autonomous Driving with Multiple Expert Demonstrations

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Brain-in-the-Loop Learning for Intelligent Vehicle Decision-Making

Zhang, X., Zheng, H., Li, J., Huang, C. H., & Wang, H. (2026) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2025.3641715 · Cited by 1

Rapid Robotic Arm Path Planning Through Deep Reinforcement Learning with Human Demonstration Key Points

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Collaborative Assembly Policy Learning of a Sightless Robot

Zhang, Z., Lu, W., Yang, L., Jing, W. P., Tang, B., & Pan, J. (2025) · IEEE International Conference on Robotics and Biomimetics (ROBIO) · doi:10.1109/robio66223.2025.11377489 · Cited by 1

Self Punishment and Reward Backfill for Deep Q-Learning

Bonyadi, M. R., Wang, R., & Ziaei, M. (2020) · PubMed · doi:10.48550/arxiv.2004.05002 · Cited by 1

WMat algorithm based on Q-Learning algorithm in taxi-v2 game

Esmaeily, F., & Keyvanpour, M. R. (2020) · International Conference on Smart Cities, Internet of Things and Applications (SCIoT) · doi:10.1109/sciot50840.2020.9250211 · Cited by 1

Stable Object Reorientation using Contact Plane Registration

Li, R., Esteves, C., Makadia, A., & Agrawal, P. (2022) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra46639.2022.9811655 · Cited by 1

Managing Shaping Complexity in Reinforcement Learning with State Machines - Using Robotic Tasks with Unspecified Repetition as an Example

Mo, Y.-W., Ho, C.-K., & King, C. (2022) · IEEE International Conference on Mechatronics and Automation (ICMA) · doi:10.1109/icma54519.2022.9856243 · Cited by 1

Reinforcement Learning to Efficiently Recover Control Performance of Robots Using Imitation Learning After Failure

Kobayashi, S., & Shibuya, T. (2022) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc53654.2022.9945538 · Cited by 1

Hu-bot: promoting the cooperation between humans and mobile robots

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A Review of Deep Reinforcement Learning Exploration Methods: Prospects and Challenges for Application to Robot Attitude Control Tasks

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Enhanced Generalization Through Prioritization and Diversity in Self-Imitation Reinforcement Learning Over Procedural Environments with Sparse Rewards

Andrés, A., Zha, D., & Ser, J. D. (2023) · IEEE Symposium Series on Computational Intelligence (SSCI) · doi:10.1109/ssci52147.2023.10371796 · Cited by 1

Computationally Efficient Reinforcement Learning: Targeted Exploration leveraging Simple Rules

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Embodied Human Activity Recognition

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Multi-Task Reinforcement Learning with Cost-based HTN Planning

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Obstacle-free Trajectory Planning of an Uncertain Space Manipulator: Learning from a Fixed-Based Manipulator

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Reinforcement Learning Approaches In Open-Ended Environments

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Demonstration and offset augmented meta reinforcement learning with sparse rewards

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Towards Safe and Efficient Learning in the Wild: Guiding RL With Constrained Uncertainty-Aware Movement Primitives

Padalkar, A., Stulp, F., Neumann, G. M., & Silvério, J. (2025) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2025.3566599 · Cited by 1

Energy-Based Exploration for Reinforcement Learning of Underactuated Mechanical Systems

Taets, J., Lefebvre, T., Ostyn, F., & Crevecoeur, G. (2025) · IEEE Access · doi:10.1109/access.2025.3576394 · Cited by 1

Robust Human-Machine Teaming Through Reinforcement Learning From Failure via Sparse Reward Densification

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Modelagem e controle de pêndulo invertido com Twin Delayed Deep Deterministic Policy Gradient e Double Deep Q-Network

de Carvalho, G. J. A., de Carvalho, J. A., Roesler, H., & Borges, H. R. (2025) · Caderno Pedagógico · doi:10.54033/cadpedv22n12-136 · Cited by 1

Goal-Conditioned End-to-End Visuomotor Control for Versatile Skill Primitives

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Towards Adaptive Enterprise

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A Guided Evaluation Method for Robot Dynamic Manipulation

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PP-PG: Combining Parameter Perturbation with Policy Gradient Methods for Effective and Efficient Explorations in Deep Reinforcement Learning

Li, S., Li, M., Su, J., Chen, S., Yuan, Z., & Ye, Q. (2021) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3452008 · Cited by 1

Leveraging optimal control demonstrations in reinforcement learning for powered descent

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A noncontact robot demonstration method with human supervision

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Leveraging Expert Demonstrations in Robot Cooperation with Multi-Agent Reinforcement Learning

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LEARNING FROM DEMONSTRATIONS WITH SACR2: SOFT ACTOR-CRITIC WITH REWARD RELABELING

Martín, J. B., Chekroun, R., & Moutarde, F. (2021) · HAL · Cited by 1

Identifying efficient curricula for reinforcement learning in complex environments with a fixed computational budget

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A Heuristic Algorithm with Falling Prediction Mechanism for Pose Planning in Irregular Objects Oriented Vertical Stacking Task

Zhang, J., & Wu, J. (2021) · International Conference on Mechatronics and Machine Vision in Practice (M2VIP) · doi:10.1109/m2vip49856.2021.9665135 · Cited by 1

Sample Efficiency in Deep Reinforcement Learning based Recommender Systems with Imitation Learning

Afsar, M. M., Crump, T., & Far, B. H. (2022) · doi:10.21428/594757db.6a36bb36 · Cited by 1

Extended-Self Recognition for Autonomous Agent Based on Controllability and Predictability

Esaki, K., Matsumura, T., Yoshimura, C., & Mizuno, H. (2022) · IEEE Symposium Series on Computational Intelligence (SSCI) · doi:10.1109/ssci51031.2022.10022161 · Cited by 1

Transformer Decoder-Based Enhanced Exploration Method to Alleviate Initial Exploration Problems in Reinforcement Learning

Kyoung, D., & Sung, Y. (2023) · Sensors · doi:10.3390/s23177411 · Cited by 2

Multi-Preference Actor CriticPreprint

Durugkar, I., Hausknecht, M., Swaminathan, A., & MacAlpine, P. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1904.03295 · Cited by 1

Automated curriculum generation for Policy Gradients from DemonstrationsPreprint

Srinivasan, A., Bahdanau, D., Chevalier-Boisvert, M., & Bengio, Y. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1912.00444 · Cited by 1

Few-Shot Bayesian Imitation Learning with Logical Program PoliciesPreprint

Silver, T., Allen, K. R., Lew, A. K., Kaelbling, L. P., & Tenenbaum, J. B. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1904.06317 · Cited by 2

Hindsight Experience Replay with Kronecker Product Approximate CurvaturePreprint

Mariyappan, D. P. G., Singla, A., & Bhatnagar, S. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.06142 · Cited by 1

"What, not how": Solving an under-actuated insertion task from scratchPreprint

Vezzani, G., Neunert, M., Wulfmeier, M., Jeong, R., Lampe, T., Siegel, N., Hafner, R., Abdolmaleki, A., Riedmiller, M., & Nori, F. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.15492 · Cited by 1

Automatic Curricula via Expert DemonstrationsPreprint

Dai, S., Hofmann, A., & Williams, B. C. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.09159 · Cited by 4

End-to-End Visual Obstacle Avoidance for a Robotic Manipulator using Deep Reinforcement Learning

Sanches, F. P. (2021) · Digital Library of Theses and Dissertations (Universidade de São Paulo) · doi:10.11606/d.55.2021.tde-30082021-100712 · Cited by 1

A Closer Look at Advantage-Filtered Behavioral Cloning in High-Noise DatasetsPreprint

Grigsby, J., & Qi, Y. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.04698 · Cited by 1

Contribución al aumento de la capacidad cognitiva de los sistemas inteligentes de transporte mediante inteligencia artificial

Pérez, A. G. (2022) · doi:10.31428/10317/11206 · Cited by 1

Data-Driven Robotic Manipulation of Cloth-like Deformable Objects: Present, Challenges, and Future ProspectsPreprint

Kadi, H. A., & Terzić, K. (2022) · Preprints.org · doi:10.20944/preprints202212.0305.v1 · Cited by 1

Mobility aid for the disabled using unknown input observers and reinforcement learning

Feng, G. (2019) · theses.fr (ABES) · doi:10.70675/fee8e83dz1de1z4417za262z865555b81fc2 · Cited by 1

Race from Pixels: Evolving Neural Network Controller for Vision-Based Car Driving

Tymchenko, B. I., & Antoshchuk, S. G. (2018) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-97885-7_3

Autonomous Open-Ended Learning of Interdependent TasksPreprint

Santucci, V. G., Cartoni, E., da Silva, B. C., & Baldassarre, G. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.02690

Task Dependent Trajectory Learning from Multiple Demonstrations Using Movement Primitives

Vidaković, J., Jerbić, B., Šekoranja, B., Švaco, M., & Šuligoj, F. (2019) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-19648-6_32

Evolutionary Stochastic Policy DistillationPreprint

Sun, H., Pan, X.-Y., Dai, B., Lin, D., & Zhou, B. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2004.12909 · Cited by 1

Asking for Help with a Cost in Reinforcement Learning

Vandenhof, C. (2020) · UWSpace (University of Waterloo)

Optimizing Air-to-Air Missile Guidance using Reinforcement Learning

van Hoorn, M. (2019) · Research Repository (Delft University of Technology)

Active Hierarchical Imitation and Reinforcement LearningPreprint

Niu, Y., & Gu, Y. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.07330

Learning Memory-Dependent Continuous Control from DemonstrationsPreprint

Hou, S., Han, D., & Tani, J. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2102.09208

Achieving Sample-Efficient Learning of Long-Horizon Sparse-Reward Robotic Tasks with Base ControllersPreprint

Xin, M., Wang, G., Liu, Z., & Wang, H. (2020) · arXiv (Cornell University)

Guiding Deep Reinforcement Learning by Modelling Expert’s Planning Behavior

Ma, R., & Oyekan, J. O. (2021) · International Conference on Control, Automation and Robotics (ICCAR) · doi:10.1109/iccar52225.2021.9463325

Mixing Human Demonstrations with Self-Exploration in Experience Replay for Deep Reinforcement LearningPreprint

Klein, D., & Cosgun, A. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.06840

Impact of Domain Knowledge Quality on Inverse Reinforcement Learning

Sogabe, R., Malla, D. B., Sogabe, M., Sakamoto, K., & Sogabe, T. (2021) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-73113-7_9

Support Rather Than Assault – Cooperative Agents in Minecraft

Potts, D., MacFarlane, K., & Hall, L. E. (2021) · Electronic workshops in computing · doi:10.14236/ewic/hci2021.13

DCUR: Data Curriculum for Teaching via Samples with Reinforcement LearningPreprint

Seita, D., Gopal, A., Mandi, Z., & Canny, J. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.07380

Learning vector fields through persistent exploration for robot motion control

Júnior, W. S. F., Monteiro, N. S., & Gonçalves, V. M. (2021) · Anais do ... Simpósio Brasileiro de Automação Inteligente/Simpósio Brasileiro de Automação Inteligente · doi:10.20906/sbai.v1i1.2663

Peer Review #1 of "LORM: a novel reinforcement learning framework for biped gait control (v0.1)"

(2022) · PeerJ Computer Science · doi:10.7287/peerj-cs.927v0.1/reviews/1

Peer Review #2 of "LORM: a novel reinforcement learning framework for biped gait control (v0.1)"

(2022) · PeerJ Computer Science · doi:10.7287/peerj-cs.927v0.1/reviews/2

Peer Review #2 of "LORM: a novel reinforcement learning framework for biped gait control (v0.2)"

(2022) · PeerJ Computer Science · doi:10.7287/peerj-cs.927v0.2/reviews/2

Peer Review #1 of "LORM: a novel reinforcement learning framework for biped gait control (v0.2)"

(2022) · PeerJ Computer Science · doi:10.7287/peerj-cs.927v0.2/reviews/1

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