Built on

Robins, A.V.: Catastrophic forgetting, rehearsal and pseudorehearsal. Connect. Sci. 7
1995
Earlier work this paper cites.
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: 2009 IEEE conference on computer vision and pattern recognition. pp. 248–255. Ieee (2009)
2009
Earlier work this paper cites.
LeCun, Y., Cortes, C.: MNIST handwritten digit database (2010), http://yann.lecun.com/exdb/mnist/
2010
Earlier work this paper cites.
Bellemare, M.G., Naddaf, Y., Veness, J., Bowling, M.: The arcade learning environment: An evaluation platform for general agents. Journal of Artificial Intelligence Research Vol. 47
Original
2012
Earlier work this paper cites.
Mnih, V., Kavukcuoglu, K., Silver, D., Graves, A., Antonoglou, I., Wierstra, D., Riedmiller, M.: Playing atari with deep reinforcement learning (2013)
2013
Earlier work this paper cites.
Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., , et al.: TensorFlow: Large-scale machine learning on heterogeneous systems (2015), https://www.tensorflow.org/ , software available from tensorflow.org
2015
Earlier work this paper cites.
van Hasselt, H., Guez, A., Silver, D.: Deep reinforcement learning with double q-learning. CoRR abs/1509.06461
Original
2015
Earlier work this paper cites.
Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., et al.: Human-level control through deep reinforcement learning. Nature 518
2015
Earlier work this paper cites.
Schulman, J., Levine, S., Moritz, P., Jordan, M.I., Abbeel, P.: Trust region policy optimization. CoRR abs/1502.05477
Original
2015
Earlier work this paper cites.
Brockman, G., Cheung, V., Pettersson, L., Schneider, J., Schulman, J., Tang, J., et al.: Openai gym. CoRR abs/1606.01540
Original
2016
Earlier work this paper cites.
Kirkpatrick, J., Pascanu, R., Rabinowitz, N.C., Veness, J., Desjardins, G., Rusu, A.A., et al.: Overcoming catastrophic forgetting in neural networks. CoRR abs/1612.00796
Original
2016
Earlier work this paper cites.

Similar

Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T.P., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. CoRR abs/1602.01783
Original
2016
Cited alongside, same era.
Plappert, M.: keras-rl. https://github.com/keras-rl/keras-rl (2016)
2016
Cited alongside, same era.
Moritz, P., Nishihara, R., Wang, S., Tumanov, A., Liaw, R., Liang, E., et al.: Ray: A distributed framework for emerging AI applications. CoRR abs/1712.05889
Original
2017
Cited alongside, same era.
Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms (2017)
2017
Cited alongside, same era.
Atkinson, C., McCane, B., Szymanski, L., Robins, A.V.: Pseudo-rehearsal: Achieving deep reinforcement learning without catastrophic forgetting. CoRR abs/1812.02464
Original

Then

Schwarz, J., Altman, D., Dudzik, A., Vinyals, O., Teh, Y.W., and, R.P.: Towards a natural benchmark for continual learning (2018), https://marcpickett.com/cl2018/CL-2018_paper_48.pdf
2018
Later among the works it cites.
Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. The MIT Press, second edn. (2018), http://incompleteideas.net/book/the-book-2nd.html
2018
Later among the works it cites.
Lesort, T., Lomonaco, V., Stoian, A., Maltoni, D., Filliat, D., Díaz-Rodríguez, N.: Continual learning for robotics: Definition, framework, learning strategies, opportunities and challenges. Information Fusion 58
2019
Later among the works it cites.
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., et al.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d'Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035. Curran Associates, Inc. (2019), http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf

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2022

Avalanche RL: a Continual Reinforcement Learning Library

Lucchesi, Nicolò, Carta, Antonio, Lomonaco, Vincenzo et al.

Understand

Continual Reinforcement Learning (CRL) is a challenging setting where an agent learns to interact with an environment that is constantly changing over time (the stream of experiences).

In this paper, we describe Avalanche RL, a library for Continual Reinforcement Learning which allows to easily train agents on a continuous stream of tasks.
Avalanche RL is based on PyTorch and supports any OpenAI Gym environment.
Its design is based on Avalanche, one of the more popular continual learning libraries, which allow us to reuse a large number of continual learning strategies and improve the interaction between reinforcement learning and continual learning researchers.

Built on

Robins, A.V.: Catastrophic forgetting, rehearsal and pseudorehearsal. Connect. Sci. 7
1995
Earlier work this paper cites.
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: 2009 IEEE conference on computer vision and pattern recognition. pp. 248–255. Ieee (2009)
2009
Earlier work this paper cites.
LeCun, Y., Cortes, C.: MNIST handwritten digit database (2010), http://yann.lecun.com/exdb/mnist/
2010
Earlier work this paper cites.
Bellemare, M.G., Naddaf, Y., Veness, J., Bowling, M.: The arcade learning environment: An evaluation platform for general agents. Journal of Artificial Intelligence Research Vol. 47
Original
2012
Earlier work this paper cites.
Mnih, V., Kavukcuoglu, K., Silver, D., Graves, A., Antonoglou, I., Wierstra, D., Riedmiller, M.: Playing atari with deep reinforcement learning (2013)
2013
Earlier work this paper cites.
Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., , et al.: TensorFlow: Large-scale machine learning on heterogeneous systems (2015), https://www.tensorflow.org/ , software available from tensorflow.org
2015
Earlier work this paper cites.
van Hasselt, H., Guez, A., Silver, D.: Deep reinforcement learning with double q-learning. CoRR abs/1509.06461
Original
2015
Earlier work this paper cites.
Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., et al.: Human-level control through deep reinforcement learning. Nature 518
2015
Earlier work this paper cites.
Schulman, J., Levine, S., Moritz, P., Jordan, M.I., Abbeel, P.: Trust region policy optimization. CoRR abs/1502.05477
Original
2015
Earlier work this paper cites.
Brockman, G., Cheung, V., Pettersson, L., Schneider, J., Schulman, J., Tang, J., et al.: Openai gym. CoRR abs/1606.01540
Original
2016
Earlier work this paper cites.
Kirkpatrick, J., Pascanu, R., Rabinowitz, N.C., Veness, J., Desjardins, G., Rusu, A.A., et al.: Overcoming catastrophic forgetting in neural networks. CoRR abs/1612.00796
Original
2016
Earlier work this paper cites.

Similar

Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T.P., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. CoRR abs/1602.01783
Original
2016
Cited alongside, same era.
Plappert, M.: keras-rl. https://github.com/keras-rl/keras-rl (2016)
2016
Cited alongside, same era.
Moritz, P., Nishihara, R., Wang, S., Tumanov, A., Liaw, R., Liang, E., et al.: Ray: A distributed framework for emerging AI applications. CoRR abs/1712.05889
Original
2017
Cited alongside, same era.
Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms (2017)
2017
Cited alongside, same era.
Atkinson, C., McCane, B., Szymanski, L., Robins, A.V.: Pseudo-rehearsal: Achieving deep reinforcement learning without catastrophic forgetting. CoRR abs/1812.02464
Original

Then

Schwarz, J., Altman, D., Dudzik, A., Vinyals, O., Teh, Y.W., and, R.P.: Towards a natural benchmark for continual learning (2018), https://marcpickett.com/cl2018/CL-2018_paper_48.pdf
2018
Later among the works it cites.
Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. The MIT Press, second edn. (2018), http://incompleteideas.net/book/the-book-2nd.html
2018
Later among the works it cites.
Lesort, T., Lomonaco, V., Stoian, A., Maltoni, D., Filliat, D., Díaz-Rodríguez, N.: Continual learning for robotics: Definition, framework, learning strategies, opportunities and challenges. Information Fusion 58
2019
Later among the works it cites.
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., et al.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d'Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035. Curran Associates, Inc. (2019), http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

2018

Cited alongside, same era.

Espeholt, L., Soyer, H., Munos, R., Simonyan, K., Mnih, V., Ward, T., et al.: Impala: Scalable distributed deep-rl with importance weighted actor-learner architectures (2018)

2018

Cited alongside, same era.

Haarnoja, T., Zhou, A., Abbeel, P., Levine, S.: Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor. CoRR abs/1801.01290

Original

2018

Cited alongside, same era.

Hook, D.W., Porter, S.J., Herzog, C.: Dimensions: Building context for search and evaluation. Frontiers in Research Metrics and Analytics 3

2018

Cited alongside, same era.

Isele, D., Cosgun, A.: Selective experience replay for lifelong learning. CoRR abs/1802.10269

Original

2018

Cited alongside, same era.

Moritz, P., Nishihara, R., Wang, S., Tumanov, A., Liaw, R., Liang, E., Elibol, M., Yang, Z., Paul, W., Jordan, M.I., Stoica, I.: Ray: A distributed framework for emerging ai applications (2018)

2018

Cited alongside, same era.

Rolnick, D., Ahuja, A., Schwarz, J., Lillicrap, T.P., Wayne, G.: Experience replay for continual learning. CoRR abs/1811.11682

Original

2018

Cited alongside, same era.

Krizhevsky, A., Nair, V., Hinton, G.: Cifar-10 (canadian institute for advanced research) http://www.cs.toronto.edu/~kriz/cifar.html

Cited in the paper.

2019