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cs.AIMar 29, 2011
26
citations (OpenAlex)
authors
  • Yi Sun
  • Faustino Gomez
  • Juergen Schmidhuber
arXiv abstractPDF
paper

Planning to Be Surprised: Optimal Bayesian Exploration in Dynamic Environments

arXiv:1103.5708

Abstract

To maximize its success, an AGI typically needs to explore its initially unknown world. Is there an optimal way of doing so? Here we derive an affirmative answer for a broad class of environments.

Cited by in corpus (8)

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  • Surprise-Based Intrinsic Motivation for Deep Reinforcement Learning
  • Reinforcement Learning through Active Inference
  • Is Pessimism Provably Efficient for Offline RL?
  • POWERPLAY: Training an Increasingly General Problem Solver by Continually Searching for the Simplest Still Unsolvable Problem
  • Learning-Driven Exploration for Reinforcement Learning
  • Tutorial and Survey on Probabilistic Graphical Model and Variational Inference in Deep Reinforcement Learning
  • A Bandit Framework for Optimal Selection of Reinforcement Learning Agents
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