cluster #6

Papers

• Google Research Football: A Novel Reinforcement Learning Environment (2019)
  Karol Kurach, Anton Raichuk, Piotr Stańczyk, et al.
  17.22
• Bridging Evolutionary Algorithms And Reinforcement Learning: A Comprehensive Survey On Hybrid Algorithms (2024)
  Pengyi Li, Jianye Hao, Hongyao Tang, et al.
  17.05
• Deep RTS: A Game Environment For Deep Reinforcement Learning In Real-time Strategy Games (2018)
  Per-Arne Andersen, Morten Goodwin, Ole-Christoffer Granmo
  16.97
• Starcraft Micromanagement With Reinforcement Learning And Curriculum Transfer Learning (2018)
  Kun Shao, Yuanheng Zhu, Dongbin Zhao
  16.19
• Shared Autonomy Via Deep Reinforcement Learning (2018)
  Siddharth Reddy, Anca D. Dragan, Sergey Levine
  15.40
• Alphastar: An Evolutionary Computation Perspective (2019)
  Kai Arulkumaran, Antoine Cully, Julian Togelius
  15.13
• Reinforcement Learning-assisted Evolutionary Algorithm: A Survey And Research Opportunities (2023)
  Yanjie Song, Yutong Wu, Yangyang Guo, et al.
  15.03
• Interpretable Policies For Reinforcement Learning By Genetic Programming (2017)
  Daniel Hein, Steffen Udluft, Thomas A. Runkler
  14.76
• Reinforcement Learning Algorithms: An Overview And Classification (2022)
  Fadi Almahamid, Katarina Grolinger
  14.73
• Constrained Multi-objective Optimization With Deep Reinforcement Learning Assisted Operator Selection (2024)
  Fei Ming, Wenyin Gong, Ling Wang, et al.
  14.73
• Intelligent Problem-solving As Integrated Hierarchical Reinforcement Learning (2022)
  Manfred Eppe, Christian Gumbsch, Matthias Kerzel, et al.
  14.02
• Evolutionary Reinforcement Learning: A Survey (2023)
  Hui Bai, Ran Cheng, Yaochu Jin
  13.93
• Vizdoom Competitions: Playing Doom From Pixels (2018)
  Marek Wydmuch, Michał Kempka, Wojciech Jaśkowski
  13.79
• Reinforcement Learning With Perturbed Rewards (2018)
  Jingkang Wang, Yang Liu, Bo Li
  13.74
• Deep Reinforcement Learning, A Textbook (2022)
  Aske Plaat
  13.17
• Policy Search In Continuous Action Domains: An Overview (2018)
  Olivier Sigaud, Freek Stulp
  12.74
• Combining Evolution And Deep Reinforcement Learning For Policy Search: A Survey (2022)
  Olivier Sigaud
  12.25
• Hierarchical Macro Strategy Model For MOBA Game AI (2018)
  Bin Wu, Qiang Fu, Jing Liang, et al.
  12.02
• An Information-theoretic Perspective On Intrinsic Motivation In Reinforcement Learning: A Survey (2022)
  Arthur Aubret, Laetitia Matignon, Salima Hassas
  11.93
• Derivative-free Reinforcement Learning: A Review (2021)
  Hong Qian, Yang Yu
  11.85
• Diversity Policy Gradient For Sample Efficient Quality-diversity Optimization (2020)
  Thomas Pierrot, Valentin MacÉ, Félix Chalumeau, et al.
  11.58
• Autoencoder-augmented Neuroevolution For Visual Doom Playing (2017)
  Samuel Alvernaz, Julian Togelius
  11.49
• Learning Representations In Model-free Hierarchical Reinforcement Learning (2018)
  Jacob Rafati, David C. Noelle
  11.49
• Predicting Game Difficulty And Churn Without Players (2020)
  Shaghayegh Roohi, Asko Relas, Jari Takatalo, et al.
  11.29
• Winning Isn't Everything: Enhancing Game Development With Intelligent Agents (2019)
  Yunqi Zhao, Igor Borovikov, Fernando de Mesentier Silva, et al.
  11.29
• Model Predictive Control And Reinforcement Learning: A Unified Framework Based On Dynamic Programming (2024)
  Dimitri P. Bertsekas
  10.61
• Evolutionary Reinforcement Learning Of Dynamical Large Deviations (2019)
  Stephen Whitelam, Daniel Jacobson, Isaac Tamblyn
  10.21
• Alphaseq: Sequence Discovery With Deep Reinforcement Learning (2018)
  Yulin Shao, Soung Chang Liew, Taotao Wang
  10.07
• Macro Action Selection With Deep Reinforcement Learning In Starcraft (2018)
  Sijia Xu, Hongyu Kuang, Zhi Zhuang, et al.
  9.92
• Evolutionary Reinforcement Learning Via Cooperative Coevolutionary Negatively Correlated Search (2020)
  Hu Zhang, Peng Yang, Yanglong Yu, et al.
  9.92
• Bias-reduced Hindsight Experience Replay With Virtual Goal Prioritization (2019)
  Binyamin Manela, Armin Biess
  9.41
• Improving Deep Reinforcement Learning In Minecraft With Action Advice (2019)
  Spencer Frazier, Mark Riedl
  9.03
• Towards A Systems Theory Of Algorithms (2024)
  Florian Dörfler, Zhiyu He, Giuseppe Belgioioso, et al.
  8.82
• To The Noise And Back: Diffusion For Shared Autonomy (2023)
  Takuma Yoneda, Luzhe Sun, Ge Yang, et al.
  8.82
• Efficient Reinforcement Learning For Starcraft By Abstract Forward Models And Transfer Learning (2019)
  Ruo-Ze Liu, Haifeng Guo, Xiaozhong Ji, et al.
  8.60
• Parallel Exploration Via Negatively Correlated Search (2019)
  Peng Yang, Qi Yang, Ke Tang, et al.
  8.60
• Ecological Reinforcement Learning (2020)
  John D. Co-Reyes, Suvansh Sanjeev, Glen Berseth, et al.
  8.35
• Rsoccer: A Framework For Studying Reinforcement Learning In Small And Very Small Size Robot Soccer (2021)
  Felipe B. Martins, Mateus G. MacHado, Hansenclever F. Bassani, et al.
  8.35
• Diversity-inducing Policy Gradient: Using Maximum Mean Discrepancy To Find A Set Of Diverse Policies (2019)
  Muhammad A. Masood, Finale Doshi-Velez
  8.35
• Moody Learners -- Explaining Competitive Behaviour Of Reinforcement Learning Agents (2020)
  Pablo Barros, Ana Tanevska, Francisco Cruz, et al.
  8.09
• Open-world Multi-task Control Through Goal-aware Representation Learning And Adaptive Horizon Prediction (2023)
  Shaofei Cai, Zihao Wang, Xiaojian Ma, et al.
  8.09
• Agents That Listen: High-throughput Reinforcement Learning With Multiple Sensory Systems (2021)
  Shashank Hegde, Anssi Kanervisto, Aleksei Petrenko
  8.09
• Novelty Search For Deep Reinforcement Learning Policy Network Weights By Action Sequence Edit Metric Distance (2019)
  Ethan C. Jackson, Mark Daley
  8.09
• Exploration And Incentives In Reinforcement Learning (2021)
  Max Simchowitz, Aleksandrs Slivkins
  8.09
• Visual Transfer Between Atari Games Using Competitive Reinforcement Learning (2018)
  Akshita Mittel, Sowmya Munukutla, Himanshi Yadav
  7.50
• A Human Mixed Strategy Approach To Deep Reinforcement Learning (2018)
  Ngoc Duy Nguyen, Saeid Nahavandi, Thanh Nguyen
  7.50
• Long-term Visitation Value For Deep Exploration In Sparse Reward Reinforcement Learning (2020)
  Simone Parisi, Davide Tateo, Maximilian Hensel, et al.
  7.24
• All By Myself: Learning Individualized Competitive Behaviour With A Contrastive Reinforcement Learning Optimization (2023)
  Pablo Barros, Alessandra Sciutti
  7.16
• Supervised And Reinforcement Learning From Observations In Reconnaissance Blind Chess (2022)
  Timo Bertram, Johannes Fürnkranz, Martin Müller
  7.16
• Self Punishment And Reward Backfill For Deep Q-learning (2020)
  Mohammad Reza Bonyadi, Rui Wang, Maryam Ziaei
  7.16