Key papers

• Self-organizing Maps For Storage And Transfer Of Knowledge In Reinforcement Learning (2018)
  Thommen George Karimpanal, Roland Bouffanais
  13.50
• Scaling Map-elites To Deep Neuroevolution (2020)
  Cédric Colas, Joost Huizinga, Vashisht Madhavan, et al.
  12.68
• Reinforcement Learning Via Recurrent Convolutional Neural Networks (2017)
  Tanmay Shankar, Santosha K. Dwivedy, Prithwijit Guha
  7.16
• Rethinking Population-assisted Off-policy Reinforcement Learning (2023)
  Bowen Zheng, Ran Cheng
  6.77
• MILP-Evo: Closed-Loop Fully Automatic Design of MILP Solvers (2026)
  Jinbiao Nie et al.
  5.89
• Learning the Arabic Dialect Continuum as a Continuous Space: A Regression Approach to Speaker Origin Prediction (2026)
  Mohamed Aziz Khadraoui et al.
  5.01
• Reduce, Reuse, Recycle: Categories For Compositional Reinforcement Learning (2024)
  Georgios Bakirtzis, Michail Savvas, Ruihan Zhao, et al.
  2.26
• Improving Reinforcement Learning Efficiency With Auxiliary Tasks In Non-visual Environments: A Comparison (2023)
  Moritz Lange, Noah Krystiniak, Raphael C. Engelhardt, et al.
  2.26
• Memory Sequence Length Of Data Sampling Impacts The Adaptation Of Meta-reinforcement Learning Agents (2024)
  Menglong Zhang, Fuyuan Qian, Quanying Liu
  2.26
• Dyna-Think: Synergizing Reasoning, Acting, and World Model Simulation in AI Agents (2025)
  Xiao Yu et al.
  1.28
• ELMUR: External Layer Memory With Update/rewrite For Long-horizon RL Problems (2025)
  Egor Cherepanov, Alexey K. Kovalev, Aleksandr I. Panov
  1.28
• Adapting World Models With Latent-state Dynamics Residuals (2025)
  Jb Lanier, Kyungmin Kim, Armin Karamzade, et al.
  1.28
• Dynamic Mixture Of Progressive Parameter-efficient Expert Library For Lifelong Robot Learning (2025)
  Yuheng Lei, Sitong Mao, Shunbo Zhou, et al.
  1.28
• GCHR : Goal-conditioned Hindsight Regularization For Sample-efficient Reinforcement Learning (2025)
  Xing Lei, Wenyan Yang, Kaiqiang Ke, et al.
  1.28
• TW-CRL: Time-weighted Contrastive Reward Learning For Efficient Inverse Reinforcement Learning (2025)
  Yuxuan Li, Yicheng Gao, Ning Yang, et al.
  1.28
• From Sparse To Dense: Toddler-inspired Reward Transition In Goal-oriented Reinforcement Learning (2025)
  Junseok Park, Hyeonseo Yang, Min Whoo Lee, et al.
  1.28
• Shaping Sparse Rewards In Reinforcement Learning: A Semi-supervised Approach (2025)
  Wenyun Li, Wenjie Huang, Chen Sun
  1.00
• Optimizing The Long-term Behaviour Of Deep Reinforcement Learning For Pushing And Grasping (2022)
  Rodrigo Chau
  0.00
• Discrete Factorial Representations As An Abstraction For Goal Conditioned Reinforcement Learning (2022)
  Riashat Islam, Hongyu Zang, Anirudh Goyal, et al.
  0.00
• Solving Offline Reinforcement Learning With Decision Tree Regression (2024)
  Prajwal Koirala, Cody Fleming
  0.00
• Curiosity Driven Exploration Of Learned Disentangled Goal Spaces (2018)
  Adrien Laversanne-Finot, Alexandre Péré, Pierre-Yves Oudeyer
  0.00
• Phasic Self-imitative Reduction For Sparse-reward Goal-conditioned Reinforcement Learning (2022)
  Yunfei Li, Tian Gao, Jiaqi Yang, et al.
  0.00
• Predictive Coding For Boosting Deep Reinforcement Learning With Sparse Rewards (2019)
  Xingyu Lu, Stas Tiomkin, Pieter Abbeel
  0.00
• CRISP: Curriculum Inducing Primitive Informed Subgoal Prediction For Hierarchical Reinforcement Learning (2023)
  Utsav Singh, Vinay P. Namboodiri
  0.00
• Learning Novel Policies For Tasks (2019)
  Yunbo Zhang, Wenhao Yu, Greg Turk
  0.00
• Hierarchical Reinforcement Learning By Discovering Intrinsic Options (2021)
  Jesse Zhang, Haonan Yu, Wei Xu
  0.00
• Curiosity-driven Experience Prioritization Via Density Estimation (2019)
  Rui Zhao, Volker Tresp
  0.00
• Proceedings of the 1st International Workshop on Robot Learning and Planning (RLP 2016) (2016)
  Nancy Amato et al.
  —
• Learning Behavior Trees with Genetic Programming in Unpredictable Environments (2020)
  Matteo Iovino et al.
  —
• Sample-efficient Reinforcement Learning in Robotic Table Tennis (2020)
  Jonas Tebbe et al.
  —
• Reward Conditioned Neural Movement Primitives for Population Based Variational Policy Optimization (2020)
  M.Tuluhan Akbulut et al.
  —
• Safe Trajectory Planning Using Reinforcement Learning for Self Driving (2020)
  Josiah Coad et al.
  —
• Efficient Heuristic Generation for Robot Path Planning with Recurrent Generative Model (2020)
  Zhaoting Li et al.
  —
• Learning from Experience for Rapid Generation of Local Car Maneuvers (2020)
  Piotr Kicki et al.
  —
• Structured Policy Representation: Imposing Stability in arbitrarily conditioned dynamic systems (2020)
  Julen Urain et al.
  —
• Experience-Based Heuristic Search: Robust Motion Planning with Deep Q-Learning (2021)
  Julian Bernhard et al.
  —
• A Bioinspired Approach-Sensitive Neural Network for Collision Detection in Cluttered and Dynamic Backgrounds (2021)
  Xiao Huang et al.
  —
• Bayesian Meta-Learning for Few-Shot Policy Adaptation Across Robotic Platforms (2021)
  Ali Ghadirzadeh et al.
  —
• Autonomous Drone Racing with Deep Reinforcement Learning (2021)
  Yunlong Song et al.
  —
• Touch-based Curiosity for Sparse-Reward Tasks (2021)
  Sai Rajeswar et al.
  —
• Capability Iteration Network for Robot Path Planning (2021)
  Buqing Nie et al.
  —
• Zero-Shot Reinforcement Learning on Graphs for Autonomous Exploration Under Uncertainty (2021)
  Fanfei Chen et al.
  —
• NeRP: Neural Rearrangement Planning for Unknown Objects (2021)
  Ahmed H. Qureshi et al.
  —
• Learning Robot Exploration Strategy with 4D Point-Clouds-like Information as Observations (2021)
  Zhaoting Li et al.
  —
• Low Dimensional State Representation Learning with Robotics Priors in Continuous Action Spaces (2021)
  Nicol\`o Botteghi et al.
  —
• RRL: Resnet as representation for Reinforcement Learning (2021)
  Rutav Shah et al.
  —
• MarsExplorer: Exploration of Unknown Terrains via Deep Reinforcement Learning and Procedurally Generated Environments (2021)
  Dimitrios I. Koutras et al.
  —
• An Improved Algorithm of Robot Path Planning in Complex Environment Based on Double DQN (2021)
  Fei Zhang et al.
  —
• Focus on Impact: Indoor Exploration with Intrinsic Motivation (2021)
  Roberto Bigazzi et al.
  —
• Example-Driven Model-Based Reinforcement Learning for Solving Long-Horizon Visuomotor Tasks (2021)
  Bohan Wu et al.
  —
• Learning of Parameters in Behavior Trees for Movement Skills (2021)
  Matthias Mayr et al.
  —
• Solving the Real Robot Challenge using Deep Reinforcement Learning (2021)
  Robert McCarthy et al.
  —
• Towards Sample-efficient Apprenticeship Learning from Suboptimal Demonstration (2021)
  Letian Chen et al.
  —
• Reinforcement Learning-Based Coverage Path Planning with Implicit Cellular Decomposition (2021)
  Javad Heydari and Olimpiya Saha and Viswanath Ganapathy
  —
• Accelerating Robotic Reinforcement Learning via Parameterized Action Primitives (2021)
  Murtaza Dalal et al.
  —
• Learning to Execute: Efficient Learning of Universal Plan-Conditioned Policies in Robotics (2021)
  Ingmar Schubert and Danny Driess and Ozgur S. Oguz and Marc Toussaint
  —
• Off Environment Evaluation Using Convex Risk Minimization (2021)
  Pulkit Katdare et al.
  —
• Benchmarking Deep Reinforcement Learning Algorithms for Vision-based Robotics (2022)
  Swagat Kumar et al.
  —
• Inducing Structure in Reward Learning by Learning Features (2022)
  Andreea Bobu et al.
  —
• Transfering Hierarchical Structure with Dual Meta Imitation Learning (2022)
  Chongkai Gao et al.
  —