arXiv:1610.01945cs.LG
Connecting Generative Adversarial Networks and Actor-Critic Methods
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Abstract
Both generative adversarial networks (GAN) in unsupervised learning and actor-critic methods in reinforcement learning (RL) have gained a reputation for being difficult to optimize. Practitioners in both fields have amassed a large number of strategies to mitigate these instabilities and improve training. Here we show that GANs can be viewed as actor-critic methods in an environment where the actor cannot affect the reward. We review the strategies for stabilizing training for each class of models, both those that generalize between the two and those that are particular to that model. We also review a number of extensions to GANs and RL algorithms with even more complicated information flow. We hope that by highlighting this formal connection we will encourage both GAN and RL communities to develop general, scalable, and stable algorithms for multilevel optimization with deep networks, and to draw inspiration across communities.
Depends on whether you see the glass half-full or half-empty. Is it a DRL actor-critic where the reward & critic happen to be half of a GAN, or is it a GAN where the generator happens to receive a RL-style loss instead of the normal discriminator loss? Actor-critic and GANs have…
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