Fetching the paper…
Reading the bibliography…
Decision Transformer, a promising approach that applies Transformer architectures to reinforcement learning, relies on causal self-attention to model sequences of states, actions, and rewards.
A framework for behavioural cloning
Bain, M. and Sammut, C · 1995
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.
Playing atari with deep reinforcement learning
Mnih, V., Kavukcuoglu, K., Silver, D., Graves, A., Antonoglou, I., Wierstra, D., and Riedmiller, M · 2013
Earlier work this paper cites.
Human-level control through deep reinforcement learning
Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., et al · 2015
Earlier work this paper cites.
Brockman, G., Cheung, V., Pettersson, L., Schneider, J., Schulman, J., Tang, J., and Zaremba, W · 2016
Earlier work this paper cites.
An optimistic perspective on offline reinforcement learning
Agarwal, R., Schuurmans, D., and Norouzi, M · 2020
Earlier work this paper cites.
D4rl: Datasets for deep data-driven reinforcement learning
Fu, J., Kumar, A., Nachum, O., Tucker, G., and Levine, S · 2020
Cited alongside, same era.
Hippo: Recurrent memory with optimal polynomial projections
Gu, A., Dao, T., Ermon, S., Rudra, A., and Ré, C · 2020
Cited alongside, same era.
Offline reinforcement learning: Tutorial, review, and perspectives on open problems
Levine, S., Kumar, A., Tucker, G., and Fu, J · 2020
Cited alongside, same era.
Decision transformer: Reinforcement learning via sequence modeling
Chen, L., Lu, K., Rajeswaran, A., Lee, K., Grover, A., Laskin, M., Abbeel, P., Srinivas, A., and Mordatch, I · 2021
Cited alongside, same era.
Combining recurrent, convolutional, and continuous-time models with linear state space layers
Gu, A., Johnson, I., Goel, K., Saab, K., Dao, T., Rudra, A., and Ré, C · 2021
Cited alongside, same era.
Mastering atari games with limited data
Ye, W., Liu, S., Kurutach, T., Abbeel, P., and Gao, Y · 2021
Later among the works it cites.
Diagonal state spaces are as effective as structured state spaces
Gupta, A., Gu, A., and Berant, J · 2022
Later among the works it cites.
Decision S4: Efficient sequence-based RL via state spaces layers
David, S. B., Zimerman, I., Nachmani, E., and Wolf, L · 2023
Later among the works it cites.
Mamba: Linear-time sequence modeling with selective state spaces
Gu, A. and Dao, T · 2023
Later among the works it cites.
Decision convformer: Local filtering in metaformer is sufficient for decision making
Kim, J., Lee, S., Kim, W., and Sung, Y · 2024
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
On the parameterization and initialization of diagonal state space models
Gu, A., Goel, K., Gupta, A., and Ré, C
Cited in the paper.
Efficiently modeling long sequences with structured state spaces
Gu, A., Goel, K., and Re, C
Cited in the paper.
Improving language understanding by generative pre-training
Radford, A., Narasimhan, K., Salimans, T., Sutskever, I., et al
Cited in the paper.