Chapter 8. Curiosity-driven exploration

This chapter covers

  • Understanding the sparse reward problem
  • Understanding how curiosity can serve as an intrinsic reward
  • Playing Super Mario Bros. from OpenAI Gym
  • Implementing an intrinsic curiosity module in PyTorch
  • Training a deep Q-network agent to successfully play Super Mario Bros. without using rewards

The fundamental reinforcement learning algorithms we have studied so far, such as deep Q-learning and policy gradient methods are very powerful techniques in a lot of situations, but they fail dramatically in other environments. Google’s DeepMind pioneered the field of deep reinforcement learning back in 2013 when they used deep Q-learning to train an agent to play multiple Atari games at superhuman ...

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