Continuous Control with Action Quantization from Demonstrations

Robert Dadashi, Léonard Hussenot, Damien Vincent, Sertan Girgin, Anton Raichuk, Matthieu Geist, Olivier Pietquin
Proceedings of the 39th International Conference on Machine Learning, PMLR 162:4537-4557, 2022.

Abstract

In this paper, we propose a novel Reinforcement Learning (RL) framework for problems with continuous action spaces: Action Quantization from Demonstrations (AQuaDem). The proposed approach consists in learning a discretization of continuous action spaces from human demonstrations. This discretization returns a set of plausible actions (in light of the demonstrations) for each input state, thus capturing the priors of the demonstrator and their multimodal behavior. By discretizing the action space, any discrete action deep RL technique can be readily applied to the continuous control problem. Experiments show that the proposed approach outperforms state-of-the-art methods such as SAC in the RL setup, and GAIL in the Imitation Learning setup. We provide a website with interactive videos: https://google-research.github.io/aquadem/ and make the code available: https://github.com/google-research/google-research/tree/master/aquadem.

Cite this Paper

BibTeX
@InProceedings{pmlr-v162-dadashi22a, title = {Continuous Control with Action Quantization from Demonstrations}, author = {Dadashi, Robert and Hussenot, L{\'e}onard and Vincent, Damien and Girgin, Sertan and Raichuk, Anton and Geist, Matthieu and Pietquin, Olivier}, booktitle = {Proceedings of the 39th International Conference on Machine Learning}, pages = {4537--4557}, year = {2022}, editor = {Chaudhuri, Kamalika and Jegelka, Stefanie and Song, Le and Szepesvari, Csaba and Niu, Gang and Sabato, Sivan}, volume = {162}, series = {Proceedings of Machine Learning Research}, month = {17--23 Jul}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v162/dadashi22a/dadashi22a.pdf}, url = {https://proceedings.mlr.press/v162/dadashi22a.html}, abstract = {In this paper, we propose a novel Reinforcement Learning (RL) framework for problems with continuous action spaces: Action Quantization from Demonstrations (AQuaDem). The proposed approach consists in learning a discretization of continuous action spaces from human demonstrations. This discretization returns a set of plausible actions (in light of the demonstrations) for each input state, thus capturing the priors of the demonstrator and their multimodal behavior. By discretizing the action space, any discrete action deep RL technique can be readily applied to the continuous control problem. Experiments show that the proposed approach outperforms state-of-the-art methods such as SAC in the RL setup, and GAIL in the Imitation Learning setup. We provide a website with interactive videos: https://google-research.github.io/aquadem/ and make the code available: https://github.com/google-research/google-research/tree/master/aquadem.} }
Endnote
%0 Conference Paper %T Continuous Control with Action Quantization from Demonstrations %A Robert Dadashi %A Léonard Hussenot %A Damien Vincent %A Sertan Girgin %A Anton Raichuk %A Matthieu Geist %A Olivier Pietquin %B Proceedings of the 39th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2022 %E Kamalika Chaudhuri %E Stefanie Jegelka %E Le Song %E Csaba Szepesvari %E Gang Niu %E Sivan Sabato %F pmlr-v162-dadashi22a %I PMLR %P 4537--4557 %U https://proceedings.mlr.press/v162/dadashi22a.html %V 162 %X In this paper, we propose a novel Reinforcement Learning (RL) framework for problems with continuous action spaces: Action Quantization from Demonstrations (AQuaDem). The proposed approach consists in learning a discretization of continuous action spaces from human demonstrations. This discretization returns a set of plausible actions (in light of the demonstrations) for each input state, thus capturing the priors of the demonstrator and their multimodal behavior. By discretizing the action space, any discrete action deep RL technique can be readily applied to the continuous control problem. Experiments show that the proposed approach outperforms state-of-the-art methods such as SAC in the RL setup, and GAIL in the Imitation Learning setup. We provide a website with interactive videos: https://google-research.github.io/aquadem/ and make the code available: https://github.com/google-research/google-research/tree/master/aquadem.
APA
Dadashi, R., Hussenot, L., Vincent, D., Girgin, S., Raichuk, A., Geist, M. & Pietquin, O.. (2022). Continuous Control with Action Quantization from Demonstrations. Proceedings of the 39th International Conference on Machine Learning, in Proceedings of Machine Learning Research 162:4537-4557 Available from https://proceedings.mlr.press/v162/dadashi22a.html.

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