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In the present paper, we propose a decoder-free extension of Dreamer, a leading model-based reinforcement learning (MBRL) method from pixels.
M. Deisenroth and C. E. Rasmussen, “PILCO: A model-based and data-efficient approach to policy search,” in ICML
2011
Earlier work this paper cites.
M. G. Bellemare, Y. Naddaf, J. Veness, and M. Bowling, “The arcade learning environment: An evaluation platform for general agents,” Journal of Artificial Intelligence Research
2013
Earlier work this paper cites.
D. P. Kingma and M. Welling, “Auto-encoding variational bayes,” in ICLR
2014
Earlier work this paper cites.
K. Cho, B. Van Merriënboer, C. Gulcehre, D. Bahdanau, et al
2014
Earlier work this paper cites.
M. Abadi, A. Agarwal, P. Barham, E. Brevdo, et al
2015
Earlier work this paper cites.
K. Chua, R. Calandra, R. McAllister, and S. Levine, “Deep reinforcement learning in a handful of trials using probabilistic dynamics models,” in NeurIPS
2018
Earlier work this paper cites.
T. Haarnoja, A. Zhou, P. Abbeel, and S. Levine, “Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor,” in ICML
2018
Earlier work this paper cites.
D. Ha and J. Schmidhuber, “Recurrent world models facilitate policy evolution,” in NeurIPS
2018
Earlier work this paper cites.
Y. Tassa, Y. Doron, A. Muldal, T. Erez, Y. Li, et al
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
M. Okada and T. Taniguchi, “Variational inference MPC for Bayesian model-based reinforcement learning,” in CoRL
2019
Earlier work this paper cites.
2019
Earlier work this paper cites.
S. Bechtle, Y. Lin, A. Rai, L. Righetti, and F. Meier, “Curious iLQR: Resolving uncertainty in model-based RL,” in CoRL
2019
Earlier work this paper cites.
A. Nagabandi, K. Konolige, S. Levine, and V. Kumar, “Deep dynamics models for learning dexterous manipulation,” in CoRL
2019
Cited alongside, same era.
Y. Yang, K. Caluwaerts, A. Iscen, T. Zhang, J. Tan, and V. Sindhwani, “Data efficient reinforcement learning for legged robots,” in CoRL
2019
Cited alongside, same era.
Y. Zhang, I. Clavera, B. Tsai, and P. Abbeel, “Asynchronous methods for model-based reinforcement learning,” in CoRL
2019
Cited alongside, same era.
G. R. Williams, B. Goldfain, K. Lee, J. Gibson, J. M. Rehg, and E. A. Theodorou, “Locally weighted regression pseudo-rehearsal for adaptive model predictive control,” in CoRL
2019
Cited alongside, same era.
K. Fang, Y. Zhu, A. Garg, S. Savarese, and L. Fei-Fei, “Dynamics learning with cascaded variational inference for multi-step manipulation,” in CoRL
2019
Cited alongside, same era.
M. Okada, N. Kosaka, and T. Taniguchi, “PlaNet of the Bayesians: Reconsidering and improving deep planning network by incorporating Bayesian inference,” in IROS
2020
Closest in time.
2020
Closest in time.
T. Chen, S. Kornblith, M. Norouzi, and G. Hinton, “A simple framework for contrastive learning of visual representations,” in ICLR
2020
Closest in time.
A. Srinivas, M. Laskin, and P. Abbeel, “CURL: Contrastive unsupervised representations for reinforcement learning,” in ICML
2020
Closest in time.
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2019
Cited alongside, same era.
D. Hafner, T. Lillicrap, I. Fischer, R. Villegas, D. Ha, H. Lee, and J. Davidson, “Learning latent dynamics for planning from pixels,” in ICML
2019
Cited alongside, same era.
2019
Cited alongside, same era.
B. Poole, S. Ozair, A. v. d. Oord, A. A. Alemi, and G. Tucker, “On variational bounds of mutual information,” in ICML
2019
Cited alongside, same era.
L. Kaiser, M. Babaeizadeh, P. Milos, B. Osinski, et al
2020
Cited alongside, same era.
D. Hafner, T. Lillicrap, J. Ba, and M. Norouzi, “Dream to control: Learning behaviors by latent imagination,” ICLR
2020
Cited alongside, same era.
D. Han, K. Doya, and J. Tani, “Variational recurrent models for solving partially observable control tasks,” in ICLR
2020
Cited alongside, same era.
2020
Closest in time.
X. Ma, P. Karkus, D. Hsu, W. S. Lee, and N. Ye, “Discriminative particle filter reinforcement learning for complex partial observations,” in ICLR
2020
Closest in time.
2020
Closest in time.
2020
Closest in time.
2020
Closest in time.
2020
Closest in time.
M. Tschannen, J. Djolonga, P. K. Rubenstein, S. Gelly, and M. Lucic, “On mutual information maximization for representation learning,” ICLR
2020
Closest in time.
R. Okumura, M. Okada, and T. Taniguchi, “Domain-adversarial and -conditional state space model for imitation learning,” in IROS
2020
Closest in time.