Policy Gradient

Papers

• Distributional Soft Actor-critic: Off-policy Reinforcement Learning For Addressing Value Estimation Errors (2020)
  Jingliang Duan, Yang Guan, Shengbo Eben Li, et al.
  17.77
• Vulnerability Of Deep Reinforcement Learning To Policy Induction Attacks (2017)
  Vahid Behzadan, Arslan Munir
  15.98
• Communication-efficient Policy Gradient Methods For Distributed Reinforcement Learning (2018)
  Tianyi Chen, Kaiqing Zhang, Georgios B. Giannakis, et al.
  13.05
• Improving Coordination In Small-scale Multi-agent Deep Reinforcement Learning Through Memory-driven Communication (2019)
  Emanuele Pesce, Giovanni Montana
  12.25
• Diversity Policy Gradient For Sample Efficient Quality-diversity Optimization (2020)
  Thomas Pierrot, Valentin MacÉ, Félix Chalumeau, et al.
  11.58
• On The Sample Complexity Of Actor-critic Method For Reinforcement Learning With Function Approximation (2019)
  Harshat Kumar, Alec Koppel, Alejandro Ribeiro
  11.49
• A Multi-agent Off-policy Actor-critic Algorithm For Distributed Reinforcement Learning (2019)
  Wesley Suttle, Zhuoran Yang, Kaiqing Zhang, et al.
  11.39
• Soft Policy Gradient Method For Maximum Entropy Deep Reinforcement Learning (2019)
  Wenjie Shi, Shiji Song, Cheng Wu
  10.85
• Direct And Indirect Reinforcement Learning (2019)
  Yang Guan, Shengbo Eben Li, Jingliang Duan, et al.
  10.74
• Dual Policy Distillation (2020)
  Kwei-Herng Lai, Daochen Zha, Yuening Li, et al.
  10.61
• 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
• Policy Distillation And Value Matching In Multiagent Reinforcement Learning (2019)
  Samir Wadhwania, Dong-Ki Kim, Shayegan Omidshafiei, et al.
  10.48
• Cooperative Multi-agent Reinforcement Learning With Partial Observations (2020)
  Yan Zhang, Michael M. Zavlanos
  10.35
• WD3: Taming The Estimation Bias In Deep Reinforcement Learning (2020)
  Qiang He, Xinwen Hou
  10.21
• Sample-efficient Model-free Reinforcement Learning With Off-policy Critics (2019)
  Denis Steckelmacher, Hélène Plisnier, Diederik M. Roijers, et al.
  9.60
• Qualitative Measurements Of Policy Discrepancy For Return-based Deep Q-network (2018)
  Wenjia Meng, Qian Zheng, Long Yang, et al.
  9.59
• 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
• Convergence Of Policy Gradient Methods For Finite-horizon Exploratory Linear-quadratic Control Problems (2022)
  Michael Giegrich, Christoph Reisinger, Yufei Zhang
  9.23
• Compatible Natural Gradient Policy Search (2019)
  Joni Pajarinen, Hong Linh Thai, Riad Akrour, et al.
  9.23
• Convergence Guarantees Of Policy Optimization Methods For Markovian Jump Linear Systems (2020)
  Joao Paulo Jansch-Porto, Bin Hu, Geir Dullerud
  9.03
• Fully Asynchronous Policy Evaluation In Distributed Reinforcement Learning Over Networks (2020)
  Xingyu Sha, Jiaqi Zhang, Keyou You, et al.
  9.03
• Simple And Optimal Methods For Stochastic Variational Inequalities, II: Markovian Noise And Policy Evaluation In Reinforcement Learning (2020)
  Georgios Kotsalis, Guanghui Lan, Tianjiao Li
  8.60
• Learning First-to-spike Policies For Neuromorphic Control Using Policy Gradients (2018)
  Bleema Rosenfeld, Osvaldo Simeone, Bipin Rajendran
  8.60
• Distributed Value Function Approximation For Collaborative Multi-agent Reinforcement Learning (2020)
  Milos S. Stankovic, Marko Beko, Srdjan S. Stankovic
  8.60
• Revisiting LQR Control From The Perspective Of Receding-horizon Policy Gradient (2023)
  Xiangyuan Zhang, Tamer Başar
  8.60
• Diversity-inducing Policy Gradient: Using Maximum Mean Discrepancy To Find A Set Of Diverse Policies (2019)
  Muhammad A. Masood, Finale Doshi-Velez
  8.35
• Novelty Search For Deep Reinforcement Learning Policy Network Weights By Action Sequence Edit Metric Distance (2019)
  Ethan C. Jackson, Mark Daley
  8.09
• MULTIPOLAR: Multi-source Policy Aggregation For Transfer Reinforcement Learning Between Diverse Environmental Dynamics (2019)
  Mohammadamin Barekatain, Ryo Yonetani, Masashi Hamaya
  7.81
• Lipschitzness Is All You Need To Tame Off-policy Generative Adversarial Imitation Learning (2020)
  Lionel Blondé, Pablo Strasser, Alexandros Kalousis
  7.81
• Neural Temporal-difference And Q-learning Provably Converge To Global Optima (2019)
  Qi Cai, Zhuoran Yang, Jason D. Lee, et al.
  7.81
• Addressing Action Oscillations Through Learning Policy Inertia (2021)
  Chen Chen, Hongyao Tang, Jianye Hao, et al.
  7.81
• Local Advantage Actor-critic For Robust Multi-agent Deep Reinforcement Learning (2021)
  Yuchen Xiao, Xueguang Lyu, Christopher Amato
  7.81
• Smoothing Policies And Safe Policy Gradients (2019)
  Matteo Papini, Matteo Pirotta, Marcello Restelli
  7.50
• Policy Optimization With Stochastic Mirror Descent (2019)
  Long Yang, Yu Zhang, Gang Zheng, et al.
  7.50
• Quantum Natural Policy Gradients: Towards Sample-efficient Reinforcement Learning (2023)
  Nico Meyer, Daniel D. Scherer, Axel Plinge, et al.
  7.16
• Gradient-aware Model-based Policy Search (2019)
  Pierluca D'Oro, Alberto Maria Metelli, Andrea Tirinzoni, et al.
  6.77
• Faded-experience Trust Region Policy Optimization For Model-free Power Allocation In Interference Channel (2020)
  Mohammad G. Khoshkholgh, Halim Yanikomeroglu
  6.77
• Reinforcement Learning In Nonzero-sum Linear Quadratic Deep Structured Games: Global Convergence Of Policy Optimization (2020)
  Masoud Roudneshin, Jalal Arabneydi, Amir G. Aghdam
  6.77
• Joint Optimization Of Multi-objective Reinforcement Learning With Policy Gradient Based Algorithm (2021)
  Qinbo Bai, Mridul Agarwal, Vaneet Aggarwal
  6.34
• Linear Convergence Of Entropy-regularized Natural Policy Gradient With Linear Function Approximation (2021)
  Semih Cayci, Niao He, R. Srikant
  6.34
• Softmax Policy Gradient Methods Can Take Exponential Time To Converge (2021)
  Gen Li, Yuting Wei, Yuejie Chi, et al.
  6.34
• An Efficient Off-policy Reinforcement Learning Algorithm For The Continuous-time LQR Problem (2023)
  Victor G. Lopez, Matthias A. Müller
  6.34
• On The Role Of Weight Sharing During Deep Option Learning (2019)
  Matthew Riemer, Ignacio Cases, Clemens Rosenbaum, et al.
  6.34
• Exploiting The Sign Of The Advantage Function To Learn Deterministic Policies In Continuous Domains (2019)
  Matthieu Zimmer, Paul Weng
  6.34
• Rethinking Adversarial Attacks In Reinforcement Learning From Policy Distribution Perspective (2025)
  Tianyang Duan, Zongyuan Zhang, Zheng Lin, et al.
  5.84
• Reinforcement Learning In Linear Quadratic Deep Structured Teams: Global Convergence Of Policy Gradient Methods (2020)
  Vida Fathi, Jalal Arabneydi, Amir G. Aghdam
  5.84
• A Further Exploration Of Deep Multi-agent Reinforcement Learning With Hybrid Action Space (2022)
  Hongzhi Hua, Guixuan Wen, Kaigui Wu
  5.84
• Proximal Gradient Temporal Difference Learning: Stable Reinforcement Learning With Polynomial Sample Complexity (2020)
  Bo Liu, Ian Gemp, Mohammad Ghavamzadeh, et al.
  5.84
• Modelling The Dynamic Joint Policy Of Teammates With Attention Multi-agent DDPG (2018)
  Hangyu Mao, Zhengchao Zhang, Zhen Xiao, et al.
  5.84
• Clipup: A Simple And Powerful Optimizer For Distribution-based Policy Evolution (2020)
  Nihat Engin Toklu, Paweł Liskowski, Rupesh Kumar Srivastava
  5.84