cluster #2

Papers

• Active Inference: Demystified And Compared (2019)
  Noor Sajid, Philip J. Ball, Thomas Parr, et al.
  15.98
• Revisiting The Arcade Learning Environment: Evaluation Protocols And Open Problems For General Agents (2017)
  Marlos C. MacHado, Marc G. Bellemare, Erik Talvitie, et al.
  15.67
• Sophisticated Inference (2020)
  Karl Friston, Lancelot da Costa, Danijar Hafner, et al.
  14.83
• Statistical Inference Of The Value Function For Reinforcement Learning In Infinite Horizon Settings (2020)
  C. Shi, S. Zhang, W. Lu, et al.
  13.14
• Human-level Control Through Directly-trained Deep Spiking Q-networks (2021)
  Guisong Liu, Wenjie Deng, Xiurui Xie, et al.
  12.40
• Reinforcement Learning And Its Connections With Neuroscience And Psychology (2020)
  Ajay Subramanian, Sharad Chitlangia, Veeky Baths
  12.25
• Adaptive Trust Region Policy Optimization: Global Convergence And Faster Rates For Regularized Mdps (2019)
  Lior Shani, Yonathan Efroni, Shie Mannor
  12.10
• Learning Offline: Memory Replay In Biological And Artificial Reinforcement Learning (2021)
  Emma L. Roscow, Raymond Chua, Rui Ponte Costa, et al.
  11.67
• Reward Maximisation Through Discrete Active Inference (2020)
  Lancelot da Costa, Noor Sajid, Thomas Parr, et al.
  10.74
• Variance Reduction In Monte Carlo Counterfactual Regret Minimization (VR-MCCFR) For Extensive Form Games Using Baselines (2018)
  Martin Schmid, Neil Burch, Marc Lanctot, et al.
  10.48
• Efficiently Breaking The Curse Of Horizon In Off-policy Evaluation With Double Reinforcement Learning (2019)
  Nathan Kallus, Masatoshi Uehara
  10.21
• Expanding The Active Inference Landscape: More Intrinsic Motivations In The Perception-action Loop (2018)
  Martin Biehl, Christian Guckelsberger, Christoph Salge, et al.
  9.92
• Deep Active Inference For Partially Observable Mdps (2020)
  Otto van Der Himst, Pablo Lanillos
  9.59
• Reinforcement Learning With Low-complexity Liquid State Machines (2019)
  Wachirawit Ponghiran, Gopalakrishnan Srinivasan, Kaushik Roy
  9.41
• The Sufficiency Of Off-policyness And Soft Clipping: PPO Is Still Insufficient According To An Off-policy Measure (2022)
  Xing Chen, Dongcui Diao, Hechang Chen, et al.
  9.23
• Bootstrapping With Models: Confidence Intervals For Off-policy Evaluation (2016)
  Josiah P. Hanna, Peter Stone, Scott Niekum
  9.23
• Compatible Natural Gradient Policy Search (2019)
  Joni Pajarinen, Hong Linh Thai, Riad Akrour, et al.
  9.23
• Online Bootstrap Inference For Policy Evaluation In Reinforcement Learning (2021)
  Pratik Ramprasad, Yuantong Li, Zhuoran Yang, et al.
  9.23
• Towards Applicable Reinforcement Learning: Improving The Generalization And Sample Efficiency With Policy Ensemble (2022)
  Zhengyu Yang, Kan Ren, Xufang Luo, et al.
  9.23
• Experience Replay Using Transition Sequences (2017)
  Thommen George Karimpanal, Roland Bouffanais
  8.82
• Learning First-to-spike Policies For Neuromorphic Control Using Policy Gradients (2018)
  Bleema Rosenfeld, Osvaldo Simeone, Bipin Rajendran
  8.60
• Vizdoom: DRQN With Prioritized Experience Replay, Double-q Learning, & Snapshot Ensembling (2018)
  Christopher Schulze, Marcus Schulze
  8.60
• Lucid Dreaming For Experience Replay: Refreshing Past States With The Current Policy (2020)
  Yunshu Du, Garrett Warnell, Assefaw Gebremedhin, et al.
  7.81
• Autoregressive Policies For Continuous Control Deep Reinforcement Learning (2019)
  Dmytro Korenkevych, A. Rupam Mahmood, Gautham Vasan, et al.
  7.50
• Reinforcement Learning Framework For Deep Brain Stimulation Study (2020)
  Dmitrii Krylov, Remi Tachet, Romain Laroche, et al.
  7.50
• Off-policy Evaluation In Doubly Inhomogeneous Environments (2023)
  Zeyu Bian, Chengchun Shi, Zhengling Qi, et al.
  7.16
• Adaptively Calibrated Critic Estimates For Deep Reinforcement Learning (2021)
  Nicolai Dorka, Tim Welschehold, Joschka Boedecker, et al.
  7.16
• Conformal Off-policy Evaluation In Markov Decision Processes (2023)
  Daniele Foffano, Alessio Russo, Alexandre Proutiere
  7.16
• A Low Latency Adaptive Coding Spiking Framework For Deep Reinforcement Learning (2022)
  Lang Qin, Rui Yan, Huajin Tang
  7.16
• Branching Time Active Inference: Empirical Study And Complexity Class Analysis (2021)
  Théophile Champion, Howard Bowman, Marek Grześ
  6.77
• Faded-experience Trust Region Policy Optimization For Model-free Power Allocation In Interference Channel (2020)
  Mohammad G. Khoshkholgh, Halim Yanikomeroglu
  6.77
• Proximal Policy Optimization With Relative Pearson Divergence (2020)
  Taisuke Kobayashi
  6.77
• Neural Networks With Motivation (2019)
  Sergey A. Shuvaev, Ngoc B. Tran, Marcus Stephenson-Jones, et al.
  6.77
• Prioritized Sweeping Neural Dynaq With Multiple Predecessors, And Hippocampal Replays (2018)
  Lise Aubin, Mehdi Khamassi, Benoît Girard
  6.34
• Context Meta-reinforcement Learning Via Neuromodulation (2021)
  Eseoghene Ben-Iwhiwhu, Jeffery Dick, Nicholas A. Ketz, et al.
  6.34
• Associative Memory Based Experience Replay For Deep Reinforcement Learning (2022)
  Mengyuan Li, Arman Kazemi, Ann Franchesca Laguna, et al.
  6.34
• A Dual-memory Architecture For Reinforcement Learning On Neuromorphic Platforms (2021)
  Wilkie Olin-Ammentorp, Yury Sokolov, Maxim Bazhenov
  6.34
• An Improved Strategy For Blood Glucose Control Using Multi-step Deep Reinforcement Learning (2024)
  Weiwei Gu, Senquan Wang
  5.84
• An Introduction To Reinforcement Learning For Neuroscience (2023)
  Kristopher T. Jensen
  5.84
• Design Space Exploration Of Approximate Computing Techniques With A Reinforcement Learning Approach (2023)
  Sepide Saeedi, Alessandro Savino, Stefano di Carlo
  5.84
• Smoothed Functional-based Gradient Algorithms For Off-policy Reinforcement Learning: A Non-asymptotic Viewpoint (2021)
  Nithia Vijayan, Prashanth L. A
  5.84
• Bootstrapping A DQN Replay Memory With Synthetic Experiences (2020)
  Wenzel Baron Pilar von Pilchau, Anthony Stein, Jörg Hähner
  5.84
• Accmer: Accelerating Multi-agent Experience Replay With Cache Locality-aware Prioritization (2023)
  Kailash Gogineni, Yongsheng Mei, Peng Wei, et al.
  5.24
• Learning Expected Emphatic Traces For Deep RL (2021)
  Ray Jiang, Shangtong Zhang, Veronica Chelu, et al.
  5.24
• Effects Of Spectral Normalization In Multi-agent Reinforcement Learning (2022)
  Kinal Mehta, Anuj Mahajan, Pawan Kumar
  5.24
• Generalized Policy Improvement Algorithms With Theoretically Supported Sample Reuse (2022)
  James Queeney, Ioannis Ch. Paschalidis, Christos G. Cassandras
  5.24
• Augmented Replay Memory In Reinforcement Learning With Continuous Control (2019)
  Mirza Ramicic, Andrea Bonarini
  5.24
• Prioritizing Samples In Reinforcement Learning With Reducible Loss (2022)
  Shivakanth Sujit, Somjit Nath, Pedro H. M. Braga, et al.
  5.24
• More For Less: Safe Policy Improvement With Stronger Performance Guarantees (2023)
  Patrick Wienhöft, Marnix Suilen, Thiago D. Simão, et al.
  5.24
• Gradient Informed Proximal Policy Optimization (2023)
  Sanghyun Son, Laura Yu Zheng, Ryan Sullivan, et al.
  5.15