Key papers

• An Optimal Online Method Of Selecting Source Policies For Reinforcement Learning (2017)
  Siyuan Li, Chongjie Zhang
  9.76
• Rethinking Population-assisted Off-policy Reinforcement Learning (2023)
  Bowen Zheng, Ran Cheng
  6.77
• Market Strategy Evaluation for Prosumers in Local Electricity Markets (2026)
  Lukas Peter Wagner et al.
  5.01
• AlphaZero in Sparsely Rewarded Games: Limits and Auxiliary Supervision (2026)
  Brent Kong et al.
  4.39
• Optimal Perimeter Guarding With Heterogeneous Robot Teams: Complexity Analysis And Effective Algorithms (2019)
  Si Wei Feng, Jingjin Yu
  3.58
• Task Transfer By Preference-based Cost Learning (2018)
  Mingxuan Jing, Xiaojian Ma, Wenbing Huang, et al.
  3.58
• RIME: Robust Preference-based Reinforcement Learning With Noisy Preferences (2024)
  Jie Cheng, Gang Xiong, Xingyuan Dai, et al.
  3.16
• Variational Curriculum Reinforcement Learning For Unsupervised Discovery Of Skills (2023)
  Seongun Kim, Kyowoon Lee, Jaesik Choi
  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
• Actor-critic Pretraining For Proximal Policy Optimization (2026)
  Andreas Kernbach, Amr Elsheikh, Nicolas Grupp, et al.
  1.94
• Scaling Internal-state Policy-gradient Methods For Pomdps (2025)
  Douglas Aberdeen, Jonathan Baxter
  1.61
• Anytime Incremental \(\rho\)pomdp Planning In Continuous Spaces (2025)
  Ron Benchetrit, Idan Lev-Yehudi, Andrey Zhitnikov, et al.
  1.28
• Trajectory First: A Curriculum For Discovering Diverse Policies (2025)
  Cornelius V. Braun, Sayantan Auddy, Marc Toussaint
  1.28
• Sample Complexity Of Distributionally Robust Average-reward Reinforcement Learning (2025)
  Zijun Chen, Shengbo Wang, Nian Si
  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
• 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
• Coupled Distributional Random Expert Distillation For World Model Online Imitation Learning (2025)
  Shangzhe Li, Zhiao Huang, Hao Su
  1.22
• Supervised Reward Inference (2025)
  Will Schwarzer, Jordan Schneider, Philip S. Thomas, et al.
  1.06
• Shaping Sparse Rewards In Reinforcement Learning: A Semi-supervised Approach (2025)
  Wenyun Li, Wenjie Huang, Chen Sun
  1.00
• Efficient Model-based Reinforcement Learning Through Optimistic Thompson Sampling (2024)
  Jasmine Bayrooti, Carl Henrik Ek, Amanda Prorok
  0.00
• Conservative Safety Critics For Exploration (2020)
  Homanga Bharadhwaj, Aviral Kumar, Nicholas Rhinehart, et al.
  0.00
• Heuristic-guided Reinforcement Learning (2021)
  Ching-An Cheng, Andrey Kolobov, Adith Swaminathan
  0.00
• Intrinsic Motivation For Encouraging Synergistic Behavior (2020)
  Rohan Chitnis, Shubham Tulsiani, Saurabh Gupta, et al.
  0.00
• Discrete Factorial Representations As An Abstraction For Goal Conditioned Reinforcement Learning (2022)
  Riashat Islam, Hongyu Zang, Anirudh Goyal, et al.
  0.00
• Factored Contextual Policy Search With Bayesian Optimization (2016)
  Peter Karkus, Andras Kupcsik, David Hsu, et al.
  0.00
• Demonstration-free Autonomous Reinforcement Learning Via Implicit And Bidirectional Curriculum (2023)
  Jigang Kim, Daesol Cho, H. Jin Kim
  0.00
• Situated GAIL: Multitask Imitation Using Task-conditioned Adversarial Inverse Reinforcement Learning (2019)
  Kyoichiro Kobayashi, Takato Horii, Ryo Iwaki, et al.
  0.00
• Solving Offline Reinforcement Learning With Decision Tree Regression (2024)
  Prajwal Koirala, Cody Fleming
  0.00
• Phasic Self-imitative Reduction For Sparse-reward Goal-conditioned Reinforcement Learning (2022)
  Yunfei Li, Tian Gao, Jiaqi Yang, et al.
  0.00
• Learning Reward And Policy Jointly From Demonstration And Preference Improves Alignment (2024)
  Chenliang Li, Siliang Zeng, Zeyi Liao, et al.
  0.00
• Regularity As Intrinsic Reward For Free Play (2023)
  Cansu Sancaktar, Justus Piater, Georg Martius
  0.00
• USHER: Unbiased Sampling For Hindsight Experience Replay (2022)
  Liam Schramm, Yunfu Deng, Edgar Granados, et al.
  0.00
• Optimistic Distributionally Robust Policy Optimization (2020)
  Jun Song, Chaoyue Zhao
  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
• 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.
  —
• Action Priors for Large Action Spaces in Robotics (2021)
  Ondrej Biza et al.
  —
• PLGRIM: Hierarchical Value Learning for Large-scale Exploration in Unknown Environments (2021)
  Sung-Kyun Kim et al.
  —
• Bayesian Meta-Learning for Few-Shot Policy Adaptation Across Robotic Platforms (2021)
  Ali Ghadirzadeh et al.
  —
• Self-Imitation Learning by Planning (2021)
  Sha Luo et al.
  —
• Curious Representation Learning for Embodied Intelligence (2021)
  Yilun Du et al.
  —
• Imitation Learning via Simultaneous Optimization of Policies and Auxiliary Trajectories (2021)
  Mandy Xie et al.
  —
• Zero-Shot Reinforcement Learning on Graphs for Autonomous Exploration Under Uncertainty (2021)
  Fanfei Chen et al.
  —
• GoSafe: Globally Optimal Safe Robot Learning (2021)
  Dominik Baumann and Alonso Marco and Matteo Turchetta and Sebastian Trimpe
  —
• 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.
  —
• Optimal Stroke Learning with Policy Gradient Approach for Robotic Table Tennis (2021)
  Yapeng Gao et al.
  —
• Focus on Impact: Indoor Exploration with Intrinsic Motivation (2021)
  Roberto Bigazzi et al.
  —
• Learning of Parameters in Behavior Trees for Movement Skills (2021)
  Matthias Mayr 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
  —
• GRI: General Reinforced Imitation and its Application to Vision-Based Autonomous Driving (2021)
  Raphael Chekroun et al.
  —
• Off Environment Evaluation Using Convex Risk Minimization (2021)
  Pulkit Katdare et al.
  —