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This is an implementation of the Reinforcement Learning multi-arm-bandit experiment using different exploration techniques.

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Multi-arm Bandits Exploration

This is an bandit experiment that implements different exploration techniques for a 10-arm testbed as described in the Reinforcement Learning Book by Sutton & Barto.

The exploration techniques covered include:

  • ε-greedy
  • Optimistic Initialization
  • UCB Exploration
  • Boltzmann (Softmax) Exploration

This experiment further compares the different exploration techniques and concludes on which is better to use in different settings.

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This is an implementation of the Reinforcement Learning multi-arm-bandit experiment using different exploration techniques.

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