Offline Actor-Critic Reinforcement Learning Scales to Large Models
Abstract
We show that offline actor-critic reinforcement learning can scale to large models - such as transformers - and follows similar scaling laws as supervised learning. We find that offline actor-critic algorithms can outperform strong, supervised, behavioral cloning baselines for multi-task training on a large dataset; containing both sub-optimal and expert behavior on 132 continuous control tasks. We introduce a Perceiver-based actor-critic model and elucidate the key features needed to make offline RL work with self- and cross-attention modules. Overall, we find that: i) simple offline actor critic algorithms are a natural choice for gradually moving away from the currently predominant paradigm of behavioral cloning, and ii) via offline RL it is possible to learn multi-task policies that master many domains simultaneously, including real robotics tasks, from sub-optimal demonstrations or self-generated data.
Cite
Text
Springenberg et al. "Offline Actor-Critic Reinforcement Learning Scales to Large Models." International Conference on Machine Learning, 2024.Markdown
[Springenberg et al. "Offline Actor-Critic Reinforcement Learning Scales to Large Models." International Conference on Machine Learning, 2024.](https://mlanthology.org/icml/2024/springenberg2024icml-offline/)BibTeX
@inproceedings{springenberg2024icml-offline,
title = {{Offline Actor-Critic Reinforcement Learning Scales to Large Models}},
author = {Springenberg, Jost Tobias and Abdolmaleki, Abbas and Zhang, Jingwei and Groth, Oliver and Bloesch, Michael and Lampe, Thomas and Brakel, Philemon and Bechtle, Sarah Maria Elisabeth and Kapturowski, Steven and Hafner, Roland and Heess, Nicolas and Riedmiller, Martin},
booktitle = {International Conference on Machine Learning},
year = {2024},
pages = {46323-46350},
volume = {235},
url = {https://mlanthology.org/icml/2024/springenberg2024icml-offline/}
}