mfbpi

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• rssalessio / ModelFreeActiveExplorationRL

  This repository hosts the code accompanying the paper "Model-Free Active Exploration in Reinforcement Learning". Our study approaches the exploration problem in Reinforcement Learning (RL) from an information-theoretical viewpoint and presents a novel, efficient, and entirely model-free solution.

  best-policy-identificationexplorationmodel-freepythonreinforcement-learningdbmfbpimfbpi
  Language:Jupyter Notebook 2