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🔗 Source: proceedings.mlr.press
📊 Size: 558 KB
👤 Author: Zhaohan Daniel Guo, Bernardo Avila Pires, Bilal Piot, Jean-Bastien Grill, Florent Altché, Rémi Munos
⬇️ Downloads: 88

Summary

Predictions of Bootstrapped Latents (PBL) is a self-supervised representation learning algorithm for multitask deep reinforcement learning (RL) that builds on multistep predictive representations of future observations, focusing on capturing structured information about environment dynamics. PBL trains its representation by predicting latent embeddings of future observations, which are themselves trained to be predictive of the representations, forming a bootstrapping effect. This approach allows the agent to learn more about key aspects of environment dynamics and provides flexibility in using multimodal observations. Experiments show that PBL delivers improved performance over state-of-the-art deep RL agents in the DMLab-30 and Atari-57 multitask suites.

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Description

Predictions of Bootstrapped Latents (PBL) is a self-supervised representation learning algorithm for multitask deep reinforcement learning (RL) that builds on...

Technical Information

  • File Format: PDF
  • File Size: 558 KB
  • Pages: 20
  • Language: EN
  • Author: Zhaohan Daniel Guo, Bernardo Avila Pires, Bilal Piot, Jean-Bastien Grill, Florent Altché, Rémi Munos
  • Total Downloads: 88
  • Last Updated: 2 weeks ago

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