Papers › IGN : Implicit Generative Networks

IGN : Implicit Generative Networks

13 Jun 2022arXiv:2206.05860archive 2025-07-28

Haozheng Luo, Tianyi Wu, Feiyu Han, Zhijun Yan, Jianfen Zhang

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.

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robinzixuan/IQN_Agent officialpytorchMIT report

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Atari Games Distributional Reinforcement Learning Reinforcement Learning (RL)quantile regression regression reinforcement-learning

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