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DARLA

PyTorch implementation of the DARLA reinforcement learning pipeline, using PPO to learn a policy from the ß-VAE's latent state

DARLA Paper

https://arxiv.org/pdf/1707.08475.pdf

Pipeline

  1. Learn disentangled features of the environment using a random agent in an unsupervised domain
  2. Learn a policy for the source domain (in this case with PPO) using the learned state representation from step 1
  3. Test the policy from step 2 on the target domain

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PyTorch implementation of the DARLA reinforcement learning pipeline

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