Deconstructing Actor-Critic: A Large-scale Empirical Study of Design Components for Practitioners | Paper Citation Record

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This paper cites Mastering the game of Go without human knowledge.Nature, 550:354–359, 2017.

Deconstructing Actor-Critic: A Large-scale Empirical Study of Design Components for Practitioners Mastering the game of Go without human knowledge.Nature, 550:354–359, 2017

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Deconstructing Actor-Critic: A Large-scale Empirical Study of Design Components for Practitioners Unresolved cited work

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This paper cites Wurman, Samuel Barrett, Kenta Kawamoto, James MacGlashan, Kaushik Sub- ramanian, Thomas J.

Deconstructing Actor-Critic: A Large-scale Empirical Study of Design Components for Practitioners Wurman, Samuel Barrett, Kenta Kawamoto, James MacGlashan, Kaushik Sub- ramanian, Thomas J

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Deconstructing Actor-Critic: A Large-scale Empirical Study of Design Components for Practitioners Magnetic control of tokamak plasmas through deep reinforcement learning.Nature, 602:414–419, 2022

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Observation a1b5f11a-da62-4b10-b9cc-8799fa724a69 · outbound

This paper cites Dense reinforcement learning for safety validation of autonomous vehicles.Nature, pages 620–627, 2023.

Deconstructing Actor-Critic: A Large-scale Empirical Study of Design Components for Practitioners Dense reinforcement learning for safety validation of autonomous vehicles.Nature, pages 620–627, 2023

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This paper cites A survey of reinforcement learning for software engineering, 2025.

Deconstructing Actor-Critic: A Large-scale Empirical Study of Design Components for Practitioners A survey of reinforcement learning for software engineering, 2025

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This paper cites Rainbow: Combining im- provements in deep reinforcement learning.

Deconstructing Actor-Critic: A Large-scale Empirical Study of Design Components for Practitioners Rainbow: Combining im- provements in deep reinforcement learning

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This paper cites Noisy networks for exploration.

Deconstructing Actor-Critic: A Large-scale Empirical Study of Design Components for Practitioners Noisy networks for exploration

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This paper cites A distributional perspective on rein- forcement learning.

Deconstructing Actor-Critic: A Large-scale Empirical Study of Design Components for Practitioners A distributional perspective on rein- forcement learning

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This paper cites Prioritized Experience Replay.

Deconstructing Actor-Critic: A Large-scale Empirical Study of Design Components for Practitioners Prioritized Experience Replay

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This paper cites The arcade learning environment: An evaluation platform for general agents.Journal of artificial intelligence research, 47:253–279, 2013.

Deconstructing Actor-Critic: A Large-scale Empirical Study of Design Components for Practitioners The arcade learning environment: An evaluation platform for general agents.Journal of artificial intelligence research, 47:253–279, 2013

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This paper cites Revisiting rainbow: Promoting more insightful and inclusive deep reinforcement learning research.

Deconstructing Actor-Critic: A Large-scale Empirical Study of Design Components for Practitioners Revisiting rainbow: Promoting more insightful and inclusive deep reinforcement learning research

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This paper cites Soft actor-critic: Off- policy maximum entropy deep reinforcement learning with a stochastic actor.

Deconstructing Actor-Critic: A Large-scale Empirical Study of Design Components for Practitioners Soft actor-critic: Off- policy maximum entropy deep reinforcement learning with a stochastic actor

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This paper cites Mujoco: A physics engine for model-based control.

Deconstructing Actor-Critic: A Large-scale Empirical Study of Design Components for Practitioners Mujoco: A physics engine for model-based control

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Deconstructing Actor-Critic: A Large-scale Empirical Study of Design Components for Practitioners DeepMind Control Suite

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This paper cites Maximum a posteriori policy optimisation.

Deconstructing Actor-Critic: A Large-scale Empirical Study of Design Components for Practitioners Maximum a posteriori policy optimisation

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This paper cites Greedy actor-critic: A new conditional cross-entropy method for policy im- provement.

Deconstructing Actor-Critic: A Large-scale Empirical Study of Design Components for Practitioners Greedy actor-critic: A new conditional cross-entropy method for policy im- provement

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This paper cites Offline reinforcement learn- ing with tsallis regularization.Transactions on Machine Learning Research, 2024.

Deconstructing Actor-Critic: A Large-scale Empirical Study of Design Components for Practitioners Offline reinforcement learn- ing with tsallis regularization.Transactions on Machine Learning Research, 2024

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This paper cites Trust region policy optimization.

Deconstructing Actor-Critic: A Large-scale Empirical Study of Design Components for Practitioners Trust region policy optimization

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This paper cites Proximal Policy Optimization Algorithms.

Deconstructing Actor-Critic: A Large-scale Empirical Study of Design Components for Practitioners Proximal Policy Optimization Algorithms

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This paper cites Lillicrap, Jonathan J.

Deconstructing Actor-Critic: A Large-scale Empirical Study of Design Components for Practitioners Lillicrap, Jonathan J

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This paper cites Al-Sakkari, Ahmed Ragab, Mohamed Ali, Hanane Dagdougui, and Daria C.

Deconstructing Actor-Critic: A Large-scale Empirical Study of Design Components for Practitioners Al-Sakkari, Ahmed Ragab, Mohamed Ali, Hanane Dagdougui, and Daria C

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Deconstructing Actor-Critic: A Large-scale Empirical Study of Design Components for Practitioners Williams

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This paper cites Rupam Mahmood, and Martha White.

Deconstructing Actor-Critic: A Large-scale Empirical Study of Design Components for Practitioners Rupam Mahmood, and Martha White

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This paper cites Mirror descent and nonlinear projected subgradient methods for convex optimization.Operations Research Letters, 31(3):167–175, 2003.

Deconstructing Actor-Critic: A Large-scale Empirical Study of Design Components for Practitioners Mirror descent and nonlinear projected subgradient methods for convex optimization.Operations Research Letters, 31(3):167–175, 2003

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This paper cites Investigating the utility of mirror descent in off-policy actor-critic.

Deconstructing Actor-Critic: A Large-scale Empirical Study of Design Components for Practitioners Investigating the utility of mirror descent in off-policy actor-critic

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This paper cites Leverage the average: an analysis of regularization in rl.

Deconstructing Actor-Critic: A Large-scale Empirical Study of Design Components for Practitioners Leverage the average: an analysis of regularization in rl

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This paper cites Mirror descent policy optimization.

Deconstructing Actor-Critic: A Large-scale Empirical Study of Design Components for Practitioners Mirror descent policy optimization

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This paper cites Machado, Pablo Samuel Castro, and Nicolas Le Roux.

Deconstructing Actor-Critic: A Large-scale Empirical Study of Design Components for Practitioners Machado, Pablo Samuel Castro, and Nicolas Le Roux

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Deconstructing Actor-Critic: A Large-scale Empirical Study of Design Components for Practitioners Approximately optimal approximate reinforcement learn- ing

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This paper cites Revisiting mixture policies in entropy-regularized actor-critic.

Deconstructing Actor-Critic: A Large-scale Empirical Study of Design Components for Practitioners Revisiting mixture policies in entropy-regularized actor-critic

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Deconstructing Actor-Critic: A Large-scale Empirical Study of Design Components for Practitioners Improving stochastic policy gradi- ents in continuous control with deep reinforcement learning using the beta distribution

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Deconstructing Actor-Critic: A Large-scale Empirical Study of Design Components for Practitioners Student-t policy in reinforcement learning to acquire global optimum of robot control.Applied Intelligence, 49(12):4335–4347, 2019

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Deconstructing Actor-Critic: A Large-scale Empirical Study of Design Components for Practitioners q-exponential policy optimization

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Deconstructing Actor-Critic: A Large-scale Empirical Study of Design Components for Practitioners Policy Representation via Diffusion Probability Model for Reinforcement Learning

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Deconstructing Actor-Critic: A Large-scale Empirical Study of Design Components for Practitioners Learning a Diffusion Model Policy from Rewards via Q-Score Matching

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Deconstructing Actor-Critic: A Large-scale Empirical Study of Design Components for Practitioners Diffusion-based reinforcement learning via q-weighted variational policy opti- mization.Advances in Neural Information Processing Systems, 37:53945–53968, 2024

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Deconstructing Actor-Critic: A Large-scale Empirical Study of Design Components for Practitioners Addressing function approximation error in actor-critic methods

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Deconstructing Actor-Critic: A Large-scale Empirical Study of Design Components for Practitioners Sampling from energy- based policies using diffusion.Reinforcement Learning Journal, 6:2291–2307, 2025

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Deconstructing Actor-Critic: A Large-scale Empirical Study of Design Components for Practitioners The primacy bias in deep reinforcement learning

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Deconstructing Actor-Critic: A Large-scale Empirical Study of Design Components for Practitioners Addressing the plasticity- stability dilemma in reinforcement learning.arXiv preprint arXiv:2512.01034, 2025

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Deconstructing Actor-Critic: A Large-scale Empirical Study of Design Components for Practitioners Sample-efficient reinforcement learning by breaking the replay ratio barrier

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Deconstructing Actor-Critic: A Large-scale Empirical Study of Design Components for Practitioners Mad-td: Model-augmented data stabilizes high update ratio rl

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Observation 818b023d-dd62-452f-98d8-fb451027ef8b · outbound

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Deconstructing Actor-Critic: A Large-scale Empirical Study of Design Components for Practitioners Stochastic approximation with two time scales.Systems & Control Letters, 29(5):291–294, 1997

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Deconstructing Actor-Critic: A Large-scale Empirical Study of Design Components for Practitioners Actor-critic algorithms.Advances in neural information processing systems, 12, 1999

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Deconstructing Actor-Critic: A Large-scale Empirical Study of Design Components for Practitioners Sutton, Mohammad Ghavamzadeh, and Mark Lee

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Observation fe2f7147-e909-46e6-9b9a-7582c394ff28 · outbound

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Deconstructing Actor-Critic: A Large-scale Empirical Study of Design Components for Practitioners Likelihood ratio gradient estimation for stochastic systems.Communications of the ACM, 33(10):75–84, 1990

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Deconstructing Actor-Critic: A Large-scale Empirical Study of Design Components for Practitioners Auto-Encoding Variational Bayes

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Deconstructing Actor-Critic: A Large-scale Empirical Study of Design Components for Practitioners Categorical Reparameterization with Gumbel-Softmax

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Deconstructing Actor-Critic: A Large-scale Empirical Study of Design Components for Practitioners Variance reduction properties of the reparameterization trick

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Deconstructing Actor-Critic: A Large-scale Empirical Study of Design Components for Practitioners Pipps: Flexible model-based policy search robust to the curse of chaos

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Deconstructing Actor-Critic: A Large-scale Empirical Study of Design Components for Practitioners Pid controllers: theory, design, and tuning.The international society of measurement and control, 1995

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Deconstructing Actor-Critic: A Large-scale Empirical Study of Design Components for Practitioners Fat-to-thin policy optimization: Offline rl with sparse policies

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Deconstructing Actor-Critic: A Large-scale Empirical Study of Design Components for Practitioners Unresolved cited work

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Deconstructing Actor-Critic: A Large-scale Empirical Study of Design Components for Practitioners Unresolved cited work

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Deconstructing Actor-Critic: A Large-scale Empirical Study of Design Components for Practitioners Unresolved cited work

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