Fetching the paper…
Reading the bibliography…
Exploration is an extremely challenging problem in reinforcement learning, especially in high dimensional state and action spaces and when only sparse rewards are available.
Intrinsically motivated reinforcement learning
Chentanez, N., Barto, A. G., and Singh, S. P · 2005
Earlier work this paper cites.
Integrating reinforcement learning with human demonstrations of varying ability
Taylor, M. E., Suay, H. B., and Chernova, S · 2011
Earlier work this paper cites.
Auto-encoding variational bayes
Kingma, D. P. and Welling, M · 2013
Earlier work this paper cites.
Trust region policy optimization
Schulman, J., Levine, S., Abbeel, P., Jordan, M., and Moritz, P · 2015
Earlier work this paper cites.
Incentivizing exploration in reinforcement learning with deep predictive models
Stadie, B. C., Levine, S., and Abbeel, P · 2015
Earlier work this paper cites.
Learning robot in-hand manipulation with tactile features
Van Hoof, H., Hermans, T., Neumann, G., and Peters, J · 2015
Earlier work this paper cites.
Unifying count-based exploration and intrinsic motivation
Bellemare, M., Srinivasan, S., Ostrovski, G., Schaul, T., Saxton, D., and Munos, R · 2016
Earlier work this paper cites.
Tutorial on variational autoencoders
Doersch, C · 2016
Earlier work this paper cites.
Vime: Variational information maximizing exploration
Houthooft, R., Chen, X., Duan, Y., Schulman, J., De Turck, F., and Abbeel, P · 2016
Cited alongside, same era.
The intentional unintentional agent: Learning to solve many continuous control tasks simultaneously
Cabi, S., Colmenarejo, S. G., Hoffman, M. W., Denil, M., Wang, Z., and De Freitas, N · 2017
Cited alongside, same era.
Model-agnostic meta-learning for fast adaptation of deep networks
Finn, C., Abbeel, P., and Levine, S · 2017
Cited alongside, same era.
Noisy networks for exploration
Fortunato, M., Azar, M. G., Piot, B., Menick, J., Osband, I., Graves, A., Mnih, V., Munos, R., Hassabis, D., Pietquin, O., et al · 2017
Cited alongside, same era.
Ex2: Exploration with exemplar models for deep reinforcement learning
Fu, J., Co-Reyes, J., and Levine, S · 2017
#Exploration: A study of count-based exploration for deep reinforcement learning
Tang, H., Houthooft, R., Foote, D., Stooke, A., Chen, O. X., Duan, Y., Schulman, J., DeTurck, F., and Abbeel, P · 2017
Later among the works it cites.
Leveraging demonstrations for deep reinforcement learning on robotics problems with sparse rewards
Vecerík, M., Hester, T., Scholz, J., Wang, F., Pietquin, O., Piot, B., Heess, N., Rothörl, T., Lampe, T., and Riedmiller, M. A · 2017
Later among the works it cites.
Model-based reinforcement learning via meta-policy optimization
Clavera, I., Rothfuss, J., Schulman, J., Fujita, Y., Asfour, T., and Abbeel, P · 2018
Later among the works it cites.
Meta-reinforcement learning of structured exploration strategies
Gupta, A., Mendonca, R., Liu, Y., Abbeel, P., and Levine, S · 2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Curiosity-driven exploration by self-supervised prediction
Pathak, D., Agrawal, P., Efros, A. A., and Darrell, T · 2017
Cited alongside, same era.
Parameter space noise for exploration
Plappert, M., Houthooft, R., Dhariwal, P., Sidor, S., Chen, R. Y., Chen, X., Asfour, T., Abbeel, P., and Andrychowicz, M · 2017
Cited alongside, same era.
Learning from demonstrations for real world reinforcement learning
Sendonaris, A. and Dulac-Arnold, C. G · 2017
Cited alongside, same era.
Hazan, E., Kakade, S. M., Singh, K., and Van Soest, A · 2018
Later among the works it cites.
Overcoming exploration in reinforcement learning with demonstrations
Nair, A., McGrew, B., Andrychowicz, M., Zaremba, W., and Abbeel, P · 2018
Later among the works it cites.
Learning complex dexterous manipulation with deep reinforcement learning and demonstrations
Rajeswaran, A., Kumar, V., Gupta, A., Vezzani, G., Schulman, J., Todorov, E., and Levine, S · 2018
Later among the works it cites.