Stealing That Free Lunch: Exposing the Limits of Dyna-Style Reinforcement Learning | Paper Citation Record

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This paper cites write newline.

Stealing That Free Lunch: Exposing the Limits of Dyna-Style Reinforcement Learning write newline

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This paper cites S., Courville, A., and Bellemare, M.

Stealing That Free Lunch: Exposing the Limits of Dyna-Style Reinforcement Learning S., Courville, A., and Bellemare, M

Reference 2

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Stealing That Free Lunch: Exposing the Limits of Dyna-Style Reinforcement Learning Unresolved cited work

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Stealing That Free Lunch: Exposing the Limits of Dyna-Style Reinforcement Learning Unresolved cited work

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Stealing That Free Lunch: Exposing the Limits of Dyna-Style Reinforcement Learning and Schaal, S

Reference 5

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This paper cites Layer Normalization.

Stealing That Free Lunch: Exposing the Limits of Dyna-Style Reinforcement Learning Layer Normalization

Reference 6

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This paper cites J., Smith, L., Kostrikov, I., and Levine, S.

Stealing That Free Lunch: Exposing the Limits of Dyna-Style Reinforcement Learning J., Smith, L., Kostrikov, I., and Levine, S

Reference 7

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This paper cites M., Gebru, T., McMillan-Major, A., and Shmitchell, S.

Stealing That Free Lunch: Exposing the Limits of Dyna-Style Reinforcement Learning M., Gebru, T., McMillan-Major, A., and Shmitchell, S

Reference 8

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This paper cites J., Leary, C., Maclaurin, D., Necula, G., Paszke, A., Vander P las, J., Wanderman- M ilne, S., and Zhang, Q.

Stealing That Free Lunch: Exposing the Limits of Dyna-Style Reinforcement Learning J., Leary, C., Maclaurin, D., Necula, G., Paszke, A., Vander P las, J., Wanderman- M ilne, S., and Zhang, Q

Reference 9

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This paper cites OpenAI Gym.

Stealing That Free Lunch: Exposing the Limits of Dyna-Style Reinforcement Learning OpenAI Gym

Reference 10

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This paper cites Sample-efficient reinforcement learning with stochastic ensemble value expansion.

Stealing That Free Lunch: Exposing the Limits of Dyna-Style Reinforcement Learning Sample-efficient reinforcement learning with stochastic ensemble value expansion

Reference 11

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Stealing That Free Lunch: Exposing the Limits of Dyna-Style Reinforcement Learning G., and Silver, D

Reference 12

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Stealing That Free Lunch: Exposing the Limits of Dyna-Style Reinforcement Learning Unresolved cited work

Reference 13

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Observation 61d81e54-88b5-4d08-97bf-9c7c951a6fb0 · outbound

This paper cites Dyna-style model-based reinforcement learning with model-free policy optimization.

Stealing That Free Lunch: Exposing the Limits of Dyna-Style Reinforcement Learning Dyna-style model-based reinforcement learning with model-free policy optimization

Reference 14

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Observation da59c95c-2f3c-4fd8-a635-6538bbacadd6 · outbound

This paper cites G., and Courville, A.

Stealing That Free Lunch: Exposing the Limits of Dyna-Style Reinforcement Learning G., and Courville, A

Reference 15

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This paper cites Addressing function approximation error in actor-critic methods.

Stealing That Free Lunch: Exposing the Limits of Dyna-Style Reinforcement Learning Addressing function approximation error in actor-critic methods

Reference 16

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This paper cites Simplifying model-based rl: learning representations, latent-space models, and policies with one objective.

Stealing That Free Lunch: Exposing the Limits of Dyna-Style Reinforcement Learning Simplifying model-based rl: learning representations, latent-space models, and policies with one objective

Reference 17

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This paper cites Continuous deep q-learning with model-based acceleration.

Stealing That Free Lunch: Exposing the Limits of Dyna-Style Reinforcement Learning Continuous deep q-learning with model-based acceleration

Reference 18

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This paper cites Soft Actor-Critic Algorithms and Applications.

Stealing That Free Lunch: Exposing the Limits of Dyna-Style Reinforcement Learning Soft Actor-Critic Algorithms and Applications

Reference 19

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This paper cites Dream to control: learning behaviors by latent imagination.

Stealing That Free Lunch: Exposing the Limits of Dyna-Style Reinforcement Learning Dream to control: learning behaviors by latent imagination

Reference 20

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This paper cites Mastering diverse control tasks through world models.

Stealing That Free Lunch: Exposing the Limits of Dyna-Style Reinforcement Learning Mastering diverse control tasks through world models

Reference 21

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This paper cites A., Hosny, A., Khodakarami, F., Waldron, L., Wang, B., McIntosh, C., Goldenberg, A., Kundaje, A., Greene, C.

Stealing That Free Lunch: Exposing the Limits of Dyna-Style Reinforcement Learning A., Hosny, A., Khodakarami, F., Waldron, L., Wang, B., McIntosh, C., Goldenberg, A., Kundaje, A., Greene, C

Reference 22

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Stealing That Free Lunch: Exposing the Limits of Dyna-Style Reinforcement Learning A., Su, H., and Wang, X

Reference 23

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This paper cites The Effect of Planning Shape on Dyna-style Planning in High-dimensional State Spaces.

Stealing That Free Lunch: Exposing the Limits of Dyna-Style Reinforcement Learning The Effect of Planning Shape on Dyna-style Planning in High-dimensional State Spaces

Reference 24

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Stealing That Free Lunch: Exposing the Limits of Dyna-Style Reinforcement Learning A., Gilitschenski, I., Farahmand, A.-m., and Eaton, E

Reference 25

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This paper cites Mbpo: Model-based policy optimization, 2019.

Stealing That Free Lunch: Exposing the Limits of Dyna-Style Reinforcement Learning Mbpo: Model-based policy optimization, 2019

Reference 26

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This paper cites When to trust your model: Model-based policy optimization.

Stealing That Free Lunch: Exposing the Limits of Dyna-Style Reinforcement Learning When to trust your model: Model-based policy optimization

Reference 27

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This paper cites Position: Benchmarking is Limited in Reinforcement Learning Research.

Stealing That Free Lunch: Exposing the Limits of Dyna-Style Reinforcement Learning Position: Benchmarking is Limited in Reinforcement Learning Research

Reference 28

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Stealing That Free Lunch: Exposing the Limits of Dyna-Style Reinforcement Learning and Boedecker, J

Reference 29

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Stealing That Free Lunch: Exposing the Limits of Dyna-Style Reinforcement Learning Bidirectional model-based policy optimization

Reference 30

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Stealing That Free Lunch: Exposing the Limits of Dyna-Style Reinforcement Learning On effective scheduling ofmModel-based reinforcement Learning

Reference 31

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Stealing That Free Lunch: Exposing the Limits of Dyna-Style Reinforcement Learning Reinforcement learning with augmented data

Reference 32

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Stealing That Free Lunch: Exposing the Limits of Dyna-Style Reinforcement Learning When to Trust Your Data: Enhancing Dyna-Style Model-Based Reinforcement Learning With Data Filter

Reference 33

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Stealing That Free Lunch: Exposing the Limits of Dyna-Style Reinforcement Learning Continuous control with deep reinforcement learning

Reference 34

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Stealing That Free Lunch: Exposing the Limits of Dyna-Style Reinforcement Learning [Re] When to trust your model: model-based policy optimization

Reference 35

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Stealing That Free Lunch: Exposing the Limits of Dyna-Style Reinforcement Learning A survey on model-based reinforcement learning

Reference 36

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Stealing That Free Lunch: Exposing the Limits of Dyna-Style Reinforcement Learning Understanding and preventing capacity loss in reinforcement learning

Reference 37

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This paper cites Overestimation, overfitting, and plasticity in actor-critic: the Bitter lesson of Reinforcement learning.

Stealing That Free Lunch: Exposing the Limits of Dyna-Style Reinforcement Learning Overestimation, overfitting, and plasticity in actor-critic: the Bitter lesson of Reinforcement learning

Reference 38

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Stealing That Free Lunch: Exposing the Limits of Dyna-Style Reinforcement Learning The primacy bias in deep reinforcement learning

Reference 39

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Stealing That Free Lunch: Exposing the Limits of Dyna-Style Reinforcement Learning PyTorch: An Imperative Style, High-Performance Deep Learning Library

Reference 40

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Stealing That Free Lunch: Exposing the Limits of Dyna-Style Reinforcement Learning Mind the model, not the agent: the primacy bias in model-based RL

Reference 41

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Stealing That Free Lunch: Exposing the Limits of Dyna-Style Reinforcement Learning EPOpt : learning robust neural network policies using model ensembles

Reference 42

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Stealing That Free Lunch: Exposing the Limits of Dyna-Style Reinforcement Learning High-Dimensional Continuous Control Using Generalized Advantage Estimation

Reference 43

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Stealing That Free Lunch: Exposing the Limits of Dyna-Style Reinforcement Learning Learning off-policy with online planning

Reference 44

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Stealing That Free Lunch: Exposing the Limits of Dyna-Style Reinforcement Learning CURL : contrastive unsupervised representations for reinforcement learning

Reference 45

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Stealing That Free Lunch: Exposing the Limits of Dyna-Style Reinforcement Learning Unresolved cited work

Reference 46

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Stealing That Free Lunch: Exposing the Limits of Dyna-Style Reinforcement Learning Model regularization for stable sample rollouts

Reference 47

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Stealing That Free Lunch: Exposing the Limits of Dyna-Style Reinforcement Learning dm\_control: Software and Tasks for Continuous Control

Reference 48

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Stealing That Free Lunch: Exposing the Limits of Dyna-Style Reinforcement Learning and Schwartz, A

Reference 49

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Stealing That Free Lunch: Exposing the Limits of Dyna-Style Reinforcement Learning Mujoco: A physics engine for model-based control

Reference 50

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This paper cites A., Hussing, M., and Eaton, E.

Stealing That Free Lunch: Exposing the Limits of Dyna-Style Reinforcement Learning A., Hussing, M., and Eaton, E

Reference 51

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This paper cites Model-based policy optimization under approximate Bayesian inference.

Stealing That Free Lunch: Exposing the Limits of Dyna-Style Reinforcement Learning Model-based policy optimization under approximate Bayesian inference

Reference 52

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Stealing That Free Lunch: Exposing the Limits of Dyna-Style Reinforcement Learning Benchmarking Model-Based Reinforcement Learning

Reference 53

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This paper cites Live in the moment: learning dynamics model adapted to evolving policy.

Stealing That Free Lunch: Exposing the Limits of Dyna-Style Reinforcement Learning Live in the moment: learning dynamics model adapted to evolving policy

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:24:24.227657Z

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Observation 5156ca77-6341-43a8-a8cf-659949f2563d · outbound

This paper cites Accelerated policy learning with parallel differentiable simulation.

Stealing That Free Lunch: Exposing the Limits of Dyna-Style Reinforcement Learning Accelerated policy learning with parallel differentiable simulation

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:24:24.214542Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation a9466429-95f2-4e9c-a0c2-86a3eab5737e · outbound

This paper cites Mastering visual continuous control: improved data-augmented reinforcement learning.

Stealing That Free Lunch: Exposing the Limits of Dyna-Style Reinforcement Learning Mastering visual continuous control: improved data-augmented reinforcement learning

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:24:24.201085Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation d2fa609a-25a9-4c43-bc43-a34530cc16f4 · outbound

This paper cites Is model ensemble necessary? Model-based RL via a single model with Lipschitz regularized value function.

Stealing That Free Lunch: Exposing the Limits of Dyna-Style Reinforcement Learning Is model ensemble necessary? Model-based RL via a single model with Lipschitz regularized value function

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:24:24.185347Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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