DEAR: Disentangled Environment and Agent Representations for Reinforcement Learning without Reconstruction

S. Levine, C. Finn, T. Darrell, and P. Abbeel, “End-to-end training of deep visuomotor policies,” The Journal of Machine Learning Research , vol. 17, no. 1, pp. 1334–1373, 2016

2016

Earlier work this paper cites.

A. Gupta, V. Kumar, C. Lynch, S. Levine, and K. Hausman, “Relay policy learning: Solving long-horizon tasks via imitation and reinforcement learning,” arXiv preprint arXiv:1910.11956 , 2019

Original

2019

Earlier work this paper cites.

D. Yarats, I. Kostrikov, and R. Fergus, “Image augmentation is all you need: Regularizing deep reinforcement learning from pixels,” in International conference on learning representations , 2020

2020

Earlier work this paper cites.

W. Huang, I. Mordatch, and D. Pathak, “One policy to control them all: Shared modular policies for agent-agnostic control,” in International Conference on Machine Learning . PMLR, 2020, pp. 4455–4464

2020

Earlier work this paper cites.

A. Pore and G. Aragon-Camarasa, “On simple reactive neural networks for behaviour-based reinforcement learning,” in 2020 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2020, pp. 7477–7483

2020

Earlier work this paper cites.

M. Laskin, A. Srinivas, and P. Abbeel, “Curl: Contrastive unsupervised representations for reinforcement learning,” in International Conference on Machine Learning . PMLR, 2020, pp. 5639–5650

2020

Earlier work this paper cites.

M. Laskin, K. Lee, A. Stooke, L. Pinto, P. Abbeel, and A. Srinivas, “Reinforcement learning with augmented data,” Advances in neural information processing systems , vol. 33, pp. 19 884–19 895, 2020

2020

Earlier work this paper cites.

D. Yarats, A. Zhang, I. Kostrikov, B. Amos, J. Pineau, and R. Fergus, “Improving sample efficiency in model-free reinforcement learning from images,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 35, no. 12, 2021, pp. 10 674–10 681

2021

Cited alongside, same era.

A. Zhang, R. T. McAllister, R. Calandra, Y. Gal, and S. Levine, “Learning invariant representations for reinforcement learning without reconstruction,” in Int. Conference on Learning Representations , 2021

2021

Cited alongside, same era.

X. Wang, L. Lian, and S. X. Yu, “Unsupervised visual attention and invariance for reinforcement learning,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 6677–6687

2021

Cited alongside, same era.

D. Yarats, R. Fergus, A. Lazaric, and L. Pinto, “Mastering visual continuous control: Improved data-augmented reinforcement learning,” arXiv preprint arXiv:2107.09645 , 2021

Original

2021

Cited alongside, same era.

T. Yu, Z. Zhang, C. Lan, Y. Lu, and Z. Chen, “Mask-based latent reconstruction for reinforcement learning,” Advances in Neural Information Processing Systems , vol. 35, pp. 25 117–25 131, 2022

2022

Later among the works it cites.

H. Choi, H. Lee, S. Jeong, and D. Min, “Environment agnostic representation for visual reinforcement learning,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023

2023

Later among the works it cites.

M. Dunion, T. McInroe, K. S. Luck, J. P. Hanna, and S. V. Albrecht, “Temporal disentanglement of representations for improved generalisation in reinforcement learning,” in The Eleventh International Conference on Learning Representations , 2023

2023

Later among the works it cites.

K. Gmelin, S. Bahl, R. Mendonca, and D. Pathak, “Efficient rl via disentangled environment and agent representations,” 2023

2023

Later among the works it cites.