Fetching the paper…
Reading the bibliography…
Meta-learning algorithms can accelerate the model-based reinforcement learning (MBRL) algorithms by finding an initial set of parameters for the dynamical model such that the model can be trained to match the actual dynamics of the system with only a few data-points.
A. V. Rao, “A survey of numerical methods for optimal control,” Advances in the Astronautical Sciences , vol. 135, no. 1, pp. 497–528, 2009
2009
Earlier work this paper cites.
E. Keogh and A. Mueen, “Curse of dimensionality,” Encyclopedia of machine learning , pp. 257–258, 2010
2010
Earlier work this paper cites.
M. P. Deisenroth and C. E. Rasmussen, “PILCO: A model-based and data-efficient approach to policy search,” in Proc. of ICML , 2011
2011
Earlier work this paper cites.
R. Collobert et al. , “Natural language processing (almost) from scratch,” JMLR , vol. 12, no. Aug, pp. 2493–2537, 2011
2011
Earlier work this paper cites.
Z. I. Botev et al. , “The cross-entropy method for optimization,” in Handbook of statistics . Elsevier, 2013, vol. 31, pp. 35–59
2013
Earlier work this paper cites.
E. Coumans et al. , “Bullet physics library,” Open source: bulletphysics. org , vol. 15, no. 49, p. 5, 2013
2013
Earlier work this paper cites.
V. Mnih et al. , “Human-level control through deep reinforcement learning,” Nature , vol. 518, no. 7540, pp. 529–533, 2015
2015
Earlier work this paper cites.
A. Cully, J. Clune, D. Tarapore, and J.-B. Mouret, “Robots that can adapt like animals,” Nature , vol. 521, no. 7553, pp. 503–507, 2015
2015
Earlier work this paper cites.
M. Cutler and J. P. How, “Efficient reinforcement learning for robots using informative simulated priors,” in Proc. of ICRA , 2015
2015
Earlier work this paper cites.
J.-B. Mouret and J. Clune, “Illuminating search spaces by mapping elites,” arxiv:1504.04909 , 2015
2015
Earlier work this paper cites.
A. Cully and J.-B. Mouret, “Evolving a behavioral repertoire for a walking robot,” Evolutionary Computation , 2015
2015
Earlier work this paper cites.
J. Hollerbach, W. Khalil, and M. Gautier, Model Identification . Cham: Springer International Publishing, 2016, pp. 113–138
2016
Cited alongside, same era.
J. K. Pugh, L. B. Soros, and K. O. Stanley, “Quality diversity: A new frontier for evolutionary computation,” Frontiers in Robotics and AI , vol. 3, p. 40, 2016
2016
Cited alongside, same era.
2017
Cited alongside, same era.
C. Finn, P. Abbeel, and S. Levine, “Model-agnostic meta-learning for fast adaptation of deep networks,” in Proc. of ICML . JMLR. org, 2017, pp. 1126–1135
2017
Cited alongside, same era.
K. Chatzilygeroudis et al. , “Black-Box Data-efficient Policy Search for Robotics,” in Proc. of IROS , 2017
2017
K. Chatzilygeroudis and J.-B. Mouret, “Using Parameterized Black-Box Priors to Scale Up Model-Based Policy Search for Robotics,” in Proc. of ICRA , 2018
2018
Later among the works it cites.
A. Cully and Y. Demiris, “Quality and diversity optimization: A unifying modular framework,” IEEE Trans. on Evolutionary Computation , vol. 22, no. 2, pp. 245–259, 2018
2018
Later among the works it cites.
K. Chatzilygeroudis, V. Vassiliades, and J.-B. Mouret, “Reset-free trial-and-error learning for robot damage recovery,” Robotics and Autonomous Systems , vol. 100, pp. 236–250, 2018
2018
Later among the works it cites.
R. Pautrat, K. Chatzilygeroudis, and J.-B. Mouret, “Bayesian optimization with automatic prior selection for data-efficient direct policy search,” in Proc. of ICRA , 2018
2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
G. Williams et al. , “Information theoretic mpc for model-based reinforcement learning,” in Proc. of ICRA , 2017
2017
Cited alongside, same era.
2017
Cited alongside, same era.
M. Saveriano, Y. Yin, P. Falco, and D. Lee, “Data-efficient control policy search using residual dynamics learning,” Proc. of IROS , 2017
2017
Cited alongside, same era.
R. Kaushik, K. Chatzilygeroudis, and J.-B. Mouret, “Multi-objective model-based policy search for data-efficient learning with sparse rewards,” in Conference on Robot Learning , 2018, pp. 839–855
2018
Cited alongside, same era.
K. Chua et al. , “Deep reinforcement learning in a handful of trials using probabilistic dynamics models,” in Proc. of NIPS , 2018, pp. 4754–4765
2018
Cited alongside, same era.
A. Nagabandi et al. , “Neural network dynamics for model-based deep reinforcement learning with model-free fine-tuning,” in Proc. of ICRA , 2018, pp. 7559–7566
2018
Cited alongside, same era.
2018
Later among the works it cites.
S. Sæmundsson, K. Hofmann, and M. Deisenroth, “Meta reinforcement learning with latent variable Gaussian processes,” in Conference on Uncertainty in Artificial Intelligence , vol. 34, 2018, pp. 642–652
2018
Later among the works it cites.
2018
Later among the works it cites.
A. Nagabandi et al. , “Learning to adapt: Meta-learning for model-based control,” in Proc. of ICLR , 2019
2019
Later among the works it cites.
K. Chatzilygeroudis, V. Vassiliades, F. Stulp, S. Calinon, and J. Mouret, “A survey on policy search algorithms for learning robot controllers in a handful of trials,” IEEE Transactions on Robotics , vol. 36, no. 2, pp. 328–347, 2020
2020
Closest in time.
R. Kaushik, P. Desreumaux, and J.-B. Mouret, “Adaptive prior selection for repertoire-based online adaptation in robotics,” Frontiers in Robotics and AI , vol. 6, p. 151, 2020
2020
Closest in time.