Fetching the paper…
Reading the bibliography…
Continual reinforcement learning (CRL) refers to a naturalistic setting where an agent needs to endlessly evolve, by trial and error, to solve multiple tasks that are presented sequentially.
A markovian decision process
Bellman, R · 1957
Earlier work this paper cites.
Extensions of lipschitz mappings into a hilbert space
Johnson, W. B. and Lindenstrauss, J · 1984
Earlier work this paper cites.
Random sampling with a reservoir
Vitter, J. S · 1985
Earlier work this paper cites.
Model predictive control: Theory and practice—a survey
Garcia, C. E., Prett, D. M., and Morari, M · 1989
Earlier work this paper cites.
Integrated architectures for learning, planning, and reacting based on approximating dynamic programming
Sutton, R. S · 1990
Earlier work this paper cites.
The cross-entropy method for combinatorial and continuous optimization
Rubinstein, R · 1999
Earlier work this paper cites.
A theory of universal artificial intelligence based on algorithmic complexity
Hutter, M · 2000
Earlier work this paper cites.
A tutorial on the cross-entropy method
De Boer, P.-T., Kroese, D. P., Mannor, S., and Rubinstein, R. Y · 2005
Earlier work this paper cites.
Learning-based model predictive control for markov decision processes
Negenborn, R. R., De Schutter, B., Wiering, M. A., and Hellendoorn, H · 2005
Earlier work this paper cites.
Universal approximation using incremental constructive feedforward networks with random hidden nodes
Huang, G.-B., Chen, L., Siew, C. K., et al · 2006
Earlier work this paper cites.
The Schur complement and its applications , volume 4
Zhang, F · 2006
Earlier work this paper cites.
Online learning and online convex optimization
Shalev-Shwartz, S. et al · 2012
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.
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.
Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2015
Earlier work this paper cites.
Model predictive path integral control using covariance variable importance sampling
Williams, G., Aldrich, A., and Theodorou, E · 2015
Earlier work this paper cites.
Overcoming catastrophic forgetting in neural networks
Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A. A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al · 2017
Earlier work this paper cites.
Ai2-thor: An interactive 3d environment for visual ai
Kolve, E., Mottaghi, R., Han, W., VanderBilt, E., Weihs, L., Herrasti, A., Deitke, M., Ehsani, K., Gordon, D., Zhu, Y., et al · 2017
Earlier work this paper cites.
Continual learning through synaptic intelligence
Zenke, F., Poole, B., and Ganguli, S · 2017
Earlier work this paper cites.
Deep reinforcement learning in a handful of trials using probabilistic dynamics models
Chua, K., Calandra, R., McAllister, R., and Levine, S · 2018
Cited alongside, same era.
Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor
Haarnoja, T., Zhou, A., Abbeel, P., and Levine, S · 2018
Cited alongside, same era.
Selective experience replay for lifelong learning
Isele, D. and Cosgun, A · 2018
Cited alongside, same era.
Packnet: Adding multiple tasks to a single network by iterative pruning
Mallya, A. and Lazebnik, S · 2018
Cited alongside, same era.
Deep online learning via meta-learning: Continual adaptation for model-based rl
Nagabandi, A., Finn, C., and Levine, S · 2018
Cited alongside, same era.
Learning to learn without forgetting by maximizing transfer and minimizing interference
Class-incremental continual learning into the extended der-verse
Boschini, M., Bonicelli, L., Buzzega, P., Porrello, A., and Calderara, S · 2022
Later among the works it cites.
Forget-free continual learning with soft-winning subnetworks
Kang, H., Yoon, J., Madjid, S. R., Hwang, S. J., and Yoo, C. D · 2022
Later among the works it cites.
Same state, different task: Continual reinforcement learning without interference
Kessler, S., Parker-Holder, J., Ball, P. J., Zohren, S., and Roberts, S. J · 2022
Later among the works it cites.
Towards continual reinforcement learning: A review and perspectives
Khetarpal, K., Riemer, M., Rish, I., and Precup, D · 2022
Later among the works it cites.
Cora: Benchmarks, baselines, and metrics as a platform for continual reinforcement learning agents
Powers, S., Xing, E., Kolve, E., Mottaghi, R., and Gupta, A · 2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Riemer, M., Cases, I., Ajemian, R., Liu, M., Rish, I., Tu, Y., and Tesauro, G · 2018
Cited alongside, same era.
Progress & compress: A scalable framework for continual learning
Schwarz, J., Luketina, J., Czarnecki, W. M., Grabska-Barwinska, A., Teh, Y. W., Pascanu, R., and Hadsell, R · 2018
Cited alongside, same era.
A meta-mdp approach to exploration for lifelong reinforcement learning
Garcia, F. M. and Thomas, P · 2019
Cited alongside, same era.
Experience replay for continual learning
Rolnick, D., Ahuja, A., Schwarz, J., Lillicrap, T., and Wayne, G · 2019
Cited alongside, same era.
Meta-world: A benchmark and evaluation for multi-task and meta reinforcement learning
Yu, T., Quillen, D., He, Z., Julian, R., Hausman, K., Finn, C., and Levine, S · 2019
Cited alongside, same era.
Leveraging procedural generation to benchmark reinforcement learning
Cobbe, K., Hesse, C., Hilton, J., and Schulman, J · 2020
Cited alongside, same era.
Orthogonal gradient descent for continual learning
Farajtabar, M., Azizan, N., Mott, A., and Li, A · 2020
Cited alongside, same era.
Sancaktar, C., Blaes, S., and Martius, G · 2022
Later among the works it cites.
Autonomous reinforcement learning: Formalism and benchmarking
Sharma, A., Xu, K., Sardana, N., Gupta, A., Hausman, K., Levine, S., and Finn, C · 2022
Later among the works it cites.
ACIL: Analytic class-incremental learning with absolute memorization and privacy protection
Zhuang, H., Weng, Z., Wei, H., Xie, R., Toh, K.-A., and Lin, Z · 2022
Later among the works it cites.
Loss of plasticity in continual deep reinforcement learning
Abbas, Z., Zhao, R., Modayil, J., White, A., and Machado, M. C · 2023
Later among the works it cites.
Building a subspace of policies for scalable continual learning
Gaya, J.-B., Doan, T., Caccia, L., Soulier, L., Denoyer, L., and Raileanu, R · 2023
Later among the works it cites.
The effectiveness of world models for continual reinforcement learning
Kessler, S., Ostaszewski, M., Bortkiewicz, M. P., Żarski, M., Wolczyk, M., Parker-Holder, J., Roberts, S. J., and Miloś, P · 2023
Later among the works it cites.
Block coordinate descent on smooth manifolds: Convergence theory and twenty-one examples
Peng, L. and Vidal, R · 2023
Later among the works it cites.
The ideal continual learner: An agent that never forgets
Peng, L., Giampouras, P. V., and Vidal, R · 2023
Later among the works it cites.
Continual task allocation in meta-policy network via sparse prompting
Yang, Y., Zhou, T., Jiang, J., Long, G., and Shi, Y · 2023
Later among the works it cites.
GKEAL: Gaussian kernel embedded analytic learning for few-shot class incremental task
Zhuang, H., Weng, Z., He, R., Lin, Z., and Zeng, Z · 2023
Later among the works it cites.
A definition of continual reinforcement learning
Abel, D., Barreto, A., Van Roy, B., Precup, D., van Hasselt, H. P., and Singh, S · 2024
Later among the works it cites.
Locality sensitive sparse encoding for learning world models online
Liu, Z., Du, C., Lee, W. S., and Lin, M · 2024
Later among the works it cites.
DS-AL: A dual-stream analytic learning for exemplar-free class-incremental learning
Zhuang, H., He, R., Tong, K., Zeng, Z., Chen, C., and Lin, Z · 2024
Later among the works it cites.