Related Work

For reference, we have included below a list of some important publications on exploration in RL from the last three years. If there is other related work that you think is particularly relevant, email us at erl-leads@google.com and we'll add it to the list!

  • Aytar, Yusuf, et al. "Playing hard exploration games by watching YouTube." Advances in Neural Information Processing Systems. 2018.
  • Bellemare, Marc, et al. "Unifying count-based exploration and intrinsic motivation." Advances in Neural Information Processing Systems. 2016.
  • Burda, Yuri, et al. “Exploration by Random Network Distillation.” International Conference on Learning Representations. 2019.
  • Burda, Yuri, et al. “Large-Scale Study of Curiosity-Driven Learning.” International Conference on Learning Representations. 2019.
  • Chen, Tao, Saurabh Gupta, and Abhinav Gupta. "Learning Exploration Policies for Navigation." International Conference on Learning Representations. 2019.
  • Ecoffet, Adrien, et al. "Montezuma’s revenge solved by go-explore, a new algorithm for hard-exploration problems (sets records on pitfall, too)." 2018.
  • Fortunato, Meire, et al. "Noisy networks for exploration." International Conference on Learning Representations. 2018.
  • Fu, Justin, John Co-Reyes, and Sergey Levine. "Ex2: Exploration with exemplar models for deep reinforcement learning." Advances in Neural Information Processing Systems. 2017.
  • Houthooft, Rein, et al. "Vime: Variational information maximizing exploration." Advances in Neural Information Processing Systems. 2016.
  • Machado, Marlos C., et al. "Eigenoption Discovery through the Deep Successor Representation." International Conference on Learning Representations. 2018.
  • Osband, Ian, et al. "Deep exploration via bootstrapped DQN." Advances in Neural Information Processing Systems. 2016.
  • Osband, Ian, et al. "Randomized Prior Functions for Deep Reinforcement Learning." Advances in Neural Information Processing Systems. 2018.
  • Ostrovski, Georg, et al. "Count-based exploration with neural density models." International Conference on Machine Learning. 2017.
  • Pathak, Deepak, et al. "Curiosity-driven exploration by self-supervised prediction." International Conference on Machine Learning. 2017.
  • Pinto, Lerrel, et al. "The curious robot: Learning visual representations via physical interactions." European Conference on Computer Vision. 2016.
  • Plappert, Matthias, et al. "Parameter Space Noise for Exploration." International Conference on Learning Representations. 2018.
  • Sukhbaatar, Sainbayar, et al. "Intrinsic motivation and automatic curricula via asymmetric self-play." International Conference on Learning Representations. 2018.
  • Tang, Haoran, et al. "#Exploration: A Study of Count-Based Exploration for Deep Reinforcement Learning." Advances in Neural Information Processing Systems. 2017.
  • Wang, Rui, et al. "Paired Open-Ended Trailblazer (POET): Endlessly Generating Increasingly Complex and Diverse Learning Environments and Their Solutions." arXiv preprint arXiv:1901.01753. 2019.