mlsanityData to 14 Oct 2026

IGN : Implicit Generative Networks

arXiv 2206.05860 ↗First posted 13 Jun 2022cs.LG cs.AIcited by 1

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Abstract (arXiv)

In this work, we build recent advances in distributional reinforcement learning to give a state-of-art distributional variant of the model based on the IQN. We achieve this by using the GAN model's generator and discriminator function with the quantile regression to approximate the full quantile value for the state-action return distribution. We demonstrate improved performance on our baseline dataset - 57 Atari 2600 games in the ALE. Also, we use our algorithm to show the state-of-art training performance of risk-sensitive policies in Atari games with the policy optimization and evaluation.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 21 of its 31 references that are in the map. First posted on arXiv 13 Jun 2022.

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21 of 31 references.

References · 21

  1. 2017Attention Is All You Needcited by 60,361
  2. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  3. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,364
  4. 2022Generative Adversarial Networkscited by 5,627
  5. 2013Playing Atari with Deep Reinforcement Learningcited by 3,274
  6. 2017Improved Training of Wasserstein GANscited by 2,235
  7. 2014Conditional Generative Adversarial Netscited by 2,162
  8. 2019A Comprehensive Survey on Transfer Learningcited by 1,160
  9. 2017A Distributional Perspective on Reinforcement Learningcited by 765
  10. 2017Distributional Reinforcement Learning with Quantile Regressioncited by 474
  11. 2018Implicit Quantile Networks for Distributional Reinforcement Learningcited by 369
  12. 2018Breaking the Curse of Horizon: Infinite-Horizon Off-Policy Estimationcited by 227
9 more
  1. 2018Distributed Distributional Deterministic Policy Gradientscited by 199
  2. 2019Fully Parameterized Quantile Function for Distributional Reinforcement Learningcited by 88
  3. 2018An Analysis of Categorical Distributional Reinforcement Learningcited by 68
  4. 2016Estimating Dynamic Treatment Regimes in Mobile Health Using V-learningcited by 65
  5. 2020Statistical Inference of the Value Function for Reinforcement Learning in Infinite Horizon Settingscited by 65
  6. 2018Distributional Multivariate Policy Evaluation and Exploration with the Bellman GANcited by 16
  7. 2017The Atari Grand Challenge Datasetcited by 14
  8. 2020Statistical analysis of Wasserstein GANs with applications to time series forecastingcited by 3
  9. 2021Belief Propagation as Diffusioncited by 2

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