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In this work we identify the dormant neuron phenomenon in deep reinforcement learning, where an agent's network suffers from an increasing number of inactive neurons, thereby affecting network expressivity.
Learning to predict by the methods of temporal differences
Sutton, R. S · 1988
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.
Python reference manual
Van Rossum, G. and Drake Jr, F. L · 1995
Earlier work this paper cites.
Matplotlib: A 2d graphics environment
Hunter, J. D · 2007
Earlier work this paper cites.
Python for scientific computing
Oliphant, T. E · 2007
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Krizhevsky, A., Hinton, G., et al · 2009
Earlier work this paper cites.
Mujoco: A physics engine for model-based control
Todorov, E., Erez, T., and Tassa, Y · 2012
Earlier work this paper cites.
Online incremental feature learning with denoising autoencoders
Zhou, G., Sohn, K., and Lee, H · 2012
Earlier work this paper cites.
The arcade learning environment: An evaluation platform for general agents
Bellemare, M. G., Naddaf, Y., Veness, J., and Bowling, M · 2013
Earlier work this paper cites.
Markov decision processes: discrete stochastic dynamic programming
Puterman, M. L · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2015
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., et al · 2015
Earlier work this paper cites.
Continuous control with deep reinforcement learning
Lillicrap, T. P., Hunt, J. J., Pritzel, A., Heess, N., Erez, T., Tassa, Y., Silver, D., and Wierstra, 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.
Deep learning scaling is predictable, empirically
Hestness, J., Narang, S., Ardalani, N., Diamos, G., Jun, H., Kianinejad, H., Patwary, M., Ali, M., Yang, Y., and Zhou, Y · 2017
Earlier work this paper cites.
Jax: composable transformations of python+ numpy programs
Bradbury, J., Frostig, R., Hawkins, P., Johnson, M. J., Leary, C., Maclaurin, D., Necula, G., Paszke, A., VanderPlas, J., Wanderman-Milne, S., et al · 2018
Earlier work this paper cites.
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Earlier work this paper cites.
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Espeholt, L., Soyer, H., Munos, R., Simonyan, K., Mnih, V., Ward, T., Doron, Y., Firoiu, V., Harley, T., Dunning, I., et al · 2018
Earlier work this paper cites.
Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor
Haarnoja, T., Zhou, A., Abbeel, P., and Levine, S · 2018
Earlier work this paper cites.
Rainbow: Combining improvements in deep reinforcement learning
Hessel, M., Modayil, J., Van Hasselt, H., Schaul, T., Ostrovski, G., Dabney, W., Horgan, D., Piot, B., Azar, M., and Silver, D · 2018
Earlier work this paper cites.
Cellular plasticity in the adult murine piriform cortex: continuous maturation of dormant precursors into excitatory neurons
Rotheneichner, P., Belles, M., Benedetti, B., König, R., Dannehl, D., Kreutzer, C., Zaunmair, P., Engelhardt, M., Aigner, L., Nacher, J., et al · 2018
Earlier work this paper cites.
Reinforcement learning: An introduction
Sutton, R. S. and Barto, A. G · 2018
Earlier work this paper cites.
Deep reinforcement learning and the deadly triad
van Hasselt, H., Doron, Y., Strub, F., Hessel, M., Sonnerat, N., and Modayil, J · 2018
Earlier work this paper cites.
Lifelong learning with dynamically expandable networks
Yoon, J., Yang, E., Lee, J., and Hwang, S. J · 2018
Cited alongside, same era.
Nest: A neural network synthesis tool based on a grow-and-prune paradigm
Dai, X., Yin, H., and Jha, N. K · 2019
Cited alongside, same era.
Diagnosing bottlenecks in deep q-learning algorithms
Fu, J., Kumar, A., Soh, M., and Levine, S · 2019
Cited alongside, same era.
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Guadarrama, S., Korattikara, A., Ramirez, O., Castro, P., Holly, E., Fishman, S., Wang, K., Gonina, E., Wu, N., Kokiopoulou, E., Sbaiz, L., Smith, J., Bartók, G., Berent, J., Harris, C., Vanhoucke, V., and Brevdo, E · 2019
Cited alongside, same era.
When to trust your model: Model-based policy optimization
Janner, M., Fu, J., Zhang, M., and Levine, S · 2019
Cited alongside, same era.
Model based reinforcement learning for atari
Kaiser, Ł., Babaeizadeh, M., Miłos, P., Osiński, B., Campbell, R. H., Czechowski, K., Erhan, D., Finn, C., Kozakowski, P., Levine, S., et al · 2019
The impact of reinitialization on generalization in convolutional neural networks
Alabdulmohsin, I., Maennel, H., and Keysers, D · 2021
Later among the works it cites.
Lifting the veil on hyper-parameters for value-based deep reinforcement learning
Araújo, J. G. M., Ceron, J. S. O., and Castro, P. S · 2021
Later among the works it cites.
Single-shot pruning for offline reinforcement learning
Arnob, S. Y., Ohib, R., Plis, S., and Precup, D · 2021
Later among the works it cites.
A study on the plasticity of neural networks
Berariu, T., Czarnecki, W., De, S., Bornschein, J., Smith, S. L., Pascanu, R., and Clopath, C · 2021
Later among the works it cites.
Continual backprop: Stochastic gradient descent with persistent randomness
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When to use parametric models in reinforcement learning?
Van Hasselt, H. P., Hessel, M., and Aslanides, J · 2019
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Splitting steepest descent for growing neural architectures
Wu, L., Wang, D., and Liu, Q · 2019
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An optimistic perspective on offline reinforcement learning
Agarwal, R., Schuurmans, D., and Norouzi, M · 2020
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On warm-starting neural network training
Ash, J. and Adams, R. P · 2020
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Autonomous navigation of stratospheric balloons using reinforcement learning
Bellemare, M. G., Candido, S., Castro, P. S., Gong, J., Machado, M. C., Moitra, S., Ponda, S. S., and Wang, Z · 2020
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Functional integration of neuronal precursors in the adult murine piriform cortex
Benedetti, B., Dannehl, D., König, R., Coviello, S., Kreutzer, C., Zaunmair, P., Jakubecova, D., Weiger, T. M., Aigner, L., Nacher, J., et al · 2020
Cited alongside, same era.
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Gradmax: Growing neural networks using gradient information
Evci, U., van Merrienboer, B., Unterthiner, T., Pedregosa, F., and Vladymyrov, M · 2021
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Secant: Self-expert cloning for zero-shot generalization of visual policies
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Stabilizing deep q-learning with convnets and vision transformers under data augmentation
Hansen, N., Su, H., and Wang, X · 2021
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Dropout q-functions for doubly efficient reinforcement learning
Hiraoka, T., Imagawa, T., Hashimoto, T., Onishi, T., and Tsuruoka, Y · 2021
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A survey of generalisation in deep reinforcement learning
Kirk, R., Zhang, A., Grefenstette, E., and Rocktäschel, T · 2021
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Understanding and preventing capacity loss in reinforcement learning
Lyle, C., Rowland, M., and Dabney, W · 2021
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Knowledge evolution in neural networks
Taha, A., Shrivastava, A., and Davis, L. S · 2021
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Image augmentation is all you need: Regularizing deep reinforcement learning from pixels
Yarats, D., Kostrikov, I., and Fergus, R · 2021
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Fortuitous forgetting in connectionist networks
Zhou, H., Vani, A., Larochelle, H., and Courville, A · 2021
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Why would the brain need dormant neuronal precursors?
Benedetti, B. and Couillard-Despres, S · 2022
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The state of sparse training in deep reinforcement learning
Graesser, L., Evci, U., Elsen, E., and Castro, P. S · 2022
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An empirical study of implicit regularization in deep offline rl
Gulcehre, C., Srinivasan, S., Sygnowski, J., Ostrovski, G., Farajtabar, M., Hoffman, M., Pascanu, R., and Doucet, A · 2022
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The primacy bias in deep reinforcement learning
Nikishin, E., Schwarzer, M., D’Oro, P., Bacon, P.-L., and Courville, A · 2022
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Dynamic sparse training for deep reinforcement learning
Sokar, G., Mocanu, E., Mocanu, D. C., Pechenizkiy, M., and Stone, P · 2022
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Rlx2: Training a sparse deep reinforcement learning model from scratch
Tan, Y., Hu, P., Pan, L., and Huang, L · 2022
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When does re-initialization work?
Zaidi, S., Berariu, T., Kim, H., Bornschein, J., Clopath, C., Teh, Y. W., and Pascanu, R · 2022
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Scaling vision transformers
Zhai, X., Kolesnikov, A., Houlsby, N., and Beyer, L · 2022
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