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 model 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.
@article{arxiv.2402.05546,
title = {Offline Actor-Critic Reinforcement Learning Scales to Large Models},
author = {Jost Tobias Springenberg and Abbas Abdolmaleki and Jingwei Zhang and Oliver Groth and Michael Bloesch and Thomas Lampe and Philemon Brakel and Sarah Bechtle and Steven Kapturowski and Roland Hafner and Nicolas Heess and Martin Riedmiller},
journal= {arXiv preprint arXiv:2402.05546},
year = {2024}
}