Curiosity

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Projects

Intrinsic Curiosity

  • TODO Edit code
  • TODO Finish notion experiment tracker.

Intrinsic GSP

Gathering data through play.

The idea here would be to use playfulness (which I believe is partially driven by behavior which gets data to better learn a set of predictions).

Some notable papers for potential auxiliary objectives:

  • (Schwarzer et al. 2021)
    • Inverse dynamics modeling \((s_t, s_{t+1}) \rightarrow a_t\)
    • goal conditioned predictions
    • SSL objectives
  • Uni[mask] (from MSR)
    • Similar to the tasks used in language model modified for general sequence modeling
  • (McLeod et al. 2021) (i.e. using collections of GVFs).

Questions

  • What is the emergent behavior of agents which only optimize an curiosity objective?
    • Does it relate to play?
    • Can behaviors which emerge from this process be useful for downstream tasks?
  • What is the emergent behavior of agents which only optimize an curiosity objective?

    • Does it relate to play?
    • Can behaviors which emerge from this process be useful for downstream tasks?
  • How do we encode curiosity into an agent?

  • How does the mind-body-environment structure give us avaneues for self-curiosity.

  • Pierre-Yves Developmental RL

References

McLeod, Matthew, Chunlok Lo, Matthew Schlegel, Andrew Jacobsen, Raksha Kumaraswamy, Martha White, and Adam White. 2021. “Continual Auxiliary Task Learning.” In Advances in Neural Information Processing Systems. Curran Associates, Inc.
Schwarzer, Max, Nitarshan Rajkumar, Michael Noukhovitch, Ankesh Anand, Laurent Charlin, R Devon Hjelm, Philip Bachman, and Aaron C Courville. 2021. “Pretraining Representations for Data-Efficient Reinforcement Learning.” In Advances in Neural Information Processing Systems. Curran Associates, Inc.