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Zero-Shot Generalization of Vision-Based RL Without Data Augmentation

Created by
  • Haebom

Author

Sumeet Batra, Gaurav S. Sukhatme

Outline

In this paper, we propose the Associative Latent DisentAnglement (ALDA) model, inspired by recent advances in computational neuroscience, to address the generalization problem of vision-based reinforcement learning agents to new environments. ALDA builds on standard off-policy reinforcement learning and combines latent disentanglement with an associative memory model to achieve zero-shot generalization to challenging task variations without relying on data augmentation. Furthermore, we formally demonstrate that data augmentation is a form of weak disentanglement and discuss its implications.

Takeaways, Limitations

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Takeaways:
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We present ALDA, a novel reinforcement learning model that achieves zero-shot generalization without relying on data augmentation.
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An effective generalization strategy is presented by combining latent dissociation and associative memory models.
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Elucidate the Limitations of data augmentation techniques and emphasize the importance of potential separation.
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Limitations:
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Further research is needed to evaluate the performance and application of the ALDA model to real-world environments.
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Analysis of the computational cost and complexity of the proposed model is needed.
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Possible limitations in the range of generalization performance across different tasks and environments.
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