Revisiting Prioritized Experience Replay: A Value Perspective

Integrated architectures for learning, planning, and reacting based on approximating dynamic programming

Sutton, R. S · 1990

Earlier work this paper cites.

Self-improving reactive agents based on reinforcement learning, planning and teaching

Lin, L.-J · 1992

Earlier work this paper cites.

Q-learning

Watkins, C. J. C. H. and Dayan, P · 1992

Earlier work this paper cites.

A neural substrate of prediction and reward

Schultz, W., Dayan, P., and Montague, P. R · 1997

Earlier work this paper cites.

Understanding dopamine and reinforcement learning: the dopamine reward prediction error hypothesis

Glimcher, P. W · 2011

Earlier work this paper cites.

The role of rewarding and novel events in facilitating memory persistence in a separate spatial memory task

Salvetti, B., Morris, R. G., and Wang, S.-H · 2014

Earlier work this paper cites.

Human-level control through deep reinforcement learning

Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., and Hassabis, D · 2015

Earlier work this paper cites.

Post-learning Hippocampal Dynamics Promote Preferential Retention of Rewarding Events

Gruber, M. J., Ritchey, M., Wang, S.-f., Doss, M. K., and Ranganath, C · 2016

Earlier work this paper cites.

Prioritized experience replay

Schaul, T., Quan, J., Antonoglou, I., and Silver, D · 2016

Earlier work this paper cites.

Mastering the game of go with deep neural networks and tree search

Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., Van Den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., et al · 2016

Earlier work this paper cites.

Reinforcement learning with deep energy-based policies

Haarnoja, T., Tang, H., Abbeel, P., and Levine, S · 2017

Cited alongside, same era.

Rainbow: Combining improvements in deep reinforcement learning

Original

Hessel, M., Modayil, J., Van Hasselt, H., Schaul, T., Ostrovski, G., Dabney, W., Horgan, D., Piot, B., Azar, M., and Silver, D · 2017

Cited alongside, same era.

Equivalence between policy gradients and soft q-learning

Original

Schulman, J., Chen, X., and Abbeel, P · 2017

Cited alongside, same era.

Optimization models and applications

El Ghaoui, L · 2018

Cited alongside, same era.

Soft actor-critic algorithms and applications

Original

Haarnoja, T., Zhou, A., Hartikainen, K., Tucker, G., Ha, S., Tan, J., Kumar, V., Zhu, H., Gupta, A., Abbeel, P., et al · 2018

Cited alongside, same era.

Organizing experience: a deeper look at replay mechanisms for sample-based planning in continuous state domains

Original

Pan, Y., Zaheer, M., White, A., Patterson, A., and White, M · 2018

Later among the works it cites.

Human hippocampal replay during rest prioritizes weakly learned information and predicts memory performance

Schapiro, A. C., McDevitt, E. A., Rogers, T. T., Mednick, S. C., and Norman, K. A · 2018

Later among the works it cites.

Reconciling λ \lambda -returns with experience replay

Daley, B. and Amato, C · 2019

Later among the works it cites.

Recurrent experience replay in distributed reinforcement learning

Kapturowski, S., Ostrovski, G., Quan, J., Munos, R., and Dabney, W · 2019

Later among the works it cites.

Post-learning hippocampal replay selectively reinforces spatial memory for highly rewarded locations

Michon, F., Sun, J.-J., Kim, C. Y., Ciliberti, D., and Kloosterman, F · 2019

Later among the works it cites.