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Visualizing optimization landscapes has led to many fundamental insights in numeric optimization, and novel improvements to optimization techniques.
The arcade learning environment: An evaluation platform for general agents
Bellemare, M. G., Naddaf, Y., Veness, J., and Bowling, M · 2013
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Trust region policy optimization
Schulman, J., Levine, S., Abbeel, P., Jordan, M., and Moritz, P · 2015
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Unifying count-based exploration and intrinsic motivation
Bellemare, M., Srinivasan, S., Ostrovski, G., Schaul, T., Saxton, D., and Munos, R · 2016
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Brockman, G., Cheung, V., Pettersson, L., Schneider, J., Schulman, J., Tang, J., and Zaremba, W · 2016
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Asynchronous methods for deep reinforcement learning
Mnih, V., Badia, A. P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., and Kavukcuoglu, K · 2016
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Cooperative multi-agent control using deep reinforcement learning
Gupta, J. K., Egorov, M., and Kochenderfer, M · 2017
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Curiosity-driven exploration by self-supervised prediction
Pathak, D., Agrawal, P., Efros, A. A., and Darrell, T · 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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Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor
Haarnoja, T., Zhou, A., Abbeel, P., and Levine, S · 2018
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Rl - proximal policy optimization (ppo) explained, Dec 2018
Hui, J · 2018
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Visualizing the loss landscape of neural nets
Li, H., Xu, Z., Taylor, G., Studer, C., and Goldstein, T · 2018
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An empirical model of large-batch training
McCandlish, S., Kaplan, J., Amodei, D., and Team, O. D · 2018
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Dota 2 with large scale deep reinforcement learning
Berner, C., Brockman, G., Chan, B., Cheung, V., Dębiak, P., Dennison, C., Farhi, D., Fischer, Q., Hashme, S., Hesse, C., et al · 2019
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Exploration by random network distillation
Burda, Y., Edwards, H., Storkey, A., and Klimov, O · 2019
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A closer look at deep policy gradients
Ilyas, A., Engstrom, L., Santurkar, S., Tsipras, D., Janoos, F., Rudolph, L., and Madry, A · 2020
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Is the policy gradient a gradient?
Nota, C. and Thomas, P. S · 2020
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Analyzing reinforcement learning benchmarks with random weight guessing
Oller, D., Glasmachers, T., and Cuccu, G · 2020
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Rl baselines3 zoo
Raffin, A · 2020
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Dynamics-aware unsupervised discovery of skills
Sharma, A., Gu, S., Levine, S., Kumar, V., and Hausman, K · 2020
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Lossplot: A better way to visualize loss landscapes
Bain, R., Tokarev, M., Kothari, H., and Damineni, R · 2021
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Eysenbach, B., Gupta, A., Ibarz, J., and Levine, S · 2019
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Stable baselines3
Raffin, A., Hill, A., Ernestus, M., Gleave, A., Kanervisto, A., and Dormann, N · 2019
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Visualizing the loss landscape of actor critic methods with applications in inventory optimization
Bekci, R. Y. and Gümüş, M · 2020
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Implementation matters in deep rl: 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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Policy information capacity: Information-theoretic measure for task complexity in deep reinforcement learning
Furuta, H., Matsushima, T., Kozuno, T., Matsuo, Y., Levine, S., Nachum, O., and Gu, S. S · 2021
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A graph placement methodology for fast chip design
Mirhoseini, A., Goldie, A., Yazgan, M., Jiang, J. W., Songhori, E. M., Wang, S., Lee, Y.-J., Johnson, E., Pathak, O., Nazi, A., Pak, J., Tong, A., Srinivasa, K., Hang, W., Tuncer, E., Le, Q. V., Laudon, J., Ho, R., Carpenter, R., and Dean, J · 2021
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Training larger networks for deep reinforcement learning, 2021
Ota, K., Jha, D. K., and Kanezaki, A · 2021
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