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Proximal Policy Optimization (PPO) methods learn a policy by iteratively performing multiple mini-batch optimization epochs of a surrogate objective with one set of sampled data.
Actor-critic algorithms
Konda, V. R. and Tsitsiklis, J. N · 2000
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Policy gradient methods for reinforcement learning with function approximation
Sutton, R. S., McAllester, D. A., Singh, S. P., and Mansour, Y · 2000
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Approximately optimal approximate reinforcement learning
Kakade, S. and Langford, J · 2002
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Safe policy iteration
Pirotta, M., Restelli, M., Pecorino, A., and Calandriello, D · 2013
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Brockman, G., Cheung, V., Pettersson, L., Schneider, J., Schulman, J., Tang, J., and Zaremba, W · 2016
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Constrained policy optimization
Achiam, J., Held, D., Tamar, A., and Abbeel, P · 2017
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Emergence of locomotion behaviours in rich environments
Heess, N., TB, D., Sriram, S., Lemmon, J., Merel, J., Wayne, G., Tassa, Y., Erez, T., Wang, Z., Eslami, S., et al · 2017
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A unified view of entropy-regularized markov decision processes
Neu, G., Jonsson, A., and Gómez, V · 2017
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Proximal policy optimization algorithms
Schulman, J., Wolski, F., Dhariwal, P., Radford, A., and Klimov, O · 2017
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Impala: Scalable distributed deep-rl with importance weighted actor-learner architectures
Espeholt, L., Soyer, H., Munos, R., Simonyan, K., Mnih, V., Ward, T., Doron, Y., Firoiu, V., Harley, T., Dunning, I., et al · 2018
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Horovod: fast and easy distributed deep learning in tensorflow
Sergeev, A. and Del Balso, M · 2018
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Tassa, Y., Doron, Y., Muldal, A., Erez, T., Li, Y., Casas, D. d. L., Budden, D., Abdolmaleki, A., Merel, J., Lefrancq, A., et al · 2018
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Projections for approximate policy iteration algorithms
Akrour, R., Pajarinen, J., Peters, J., and Neumann, G · 2019
What matters in on-policy reinforcement learning? a large-scale empirical study
Andrychowicz, M., Raichuk, A., Stańczyk, P., Orsini, M., Girgin, S., Marinier, R., Hussenot, L., Geist, M., Pietquin, O., Michalski, M., et al · 2020
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Implementation matters in deep policy gradients: A case study on ppo and trpo
Engstrom, L., Ilyas, A., Santurkar, S., Tsipras, D., Janoos, F., Rudolph, L., and Madry, A · 2020
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Mirror descent policy optimization
Tomar, M., Shani, L., Efroni, Y., and Ghavamzadeh, M · 2020
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Truly proximal policy optimization
Wang, Y., He, H., and Tan, X · 2020
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Mastering complex control in moba games with deep reinforcement learning
Ye, D., Liu, Z., Sun, M., Shi, B., Zhao, P., Wu, H., Yu, H., Yang, S., Wu, X., Guo, Q., et al · 2020
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Dota 2 with large scale deep reinforcement learning
Berner, C., Brockman, G., Chan, B., Cheung, V., Debiak, P., Dennison, C., Farhi, D., Fischer, Q., Hashme, S., Hesse, C., et al · 2019
Cited alongside, same era.
A theory of regularized markov decision processes
Geist, M., Scherrer, B., and Pietquin, O · 2019
Cited alongside, same era.
Neural trust region/proximal policy optimization attains globally optimal policy
Liu, B., Cai, Q., Yang, Z., and Wang, Z · 2019
Cited alongside, same era.
Trust region policy optimization
Schulman, J., Levine, S., Abbeel, P., Jordan, M., and Moritz, P
Cited in the paper.
High-dimensional continuous control using generalized advantage estimation
Schulman, J., Moritz, P., Levine, S., Jordan, M., and Abbeel, P
Cited in the paper.
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Muesli: Combining improvements in policy optimization
Hessel, M., Danihelka, I., Viola, F., Guez, A., Schmitt, S., Sifre, L., Weber, T., Silver, D., and van Hasselt, H · 2021
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Generalized proximal policy optimization with sample reuse
Queeney, J., Paschalidis, I., and Cassandras, C · 2021
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Hinge policy optimization: Rethinking policy improvement and reinterpreting ppo
Yao, H.-Y., Hsieh, P.-C., Ho, K.-H., Hu, K.-C., Ouyang, L.-C., Wu, I., et al · 2021
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