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Imagine Networks

arXiv 2111.03048 ↗First posted 4 Nov 2021cs.AIcited by 0

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Abstract (arXiv)

In this paper, we introduce an imagine network that can simulate itself through artificial association networks. Association, deduction, and memory networks are learned, and a network is created by combining the discriminator and reinforcement learning models. This model can learn various datasets or data samples generated in environments and generate new data samples.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 5 of its 8 references that are in the map. First posted on arXiv 4 Nov 2021.

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5 of 8 references.

References · 5

  1. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,885
  2. 2015Continuous control with deep reinforcement learningcited by 3,407
  3. 2013Playing Atari with Deep Reinforcement Learningcited by 3,274
  4. 2021Deductive Association Networkscited by 2
  5. 2021Memory Association Networkscited by 1

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