Offline Actor-Critic Reinforcement Learning Scales to Large Models
Authors: name: Jost Tobias Springenberg, name: Abbas Abdolmaleki, name: Jingwei Zhang, name: Oliver Groth, name: Michael Bloesch, name: Thomas Lampe, name: Philemon Brakel, name: Sarah Bechtle, name: Steven Kapturowski, name: Roland Hafner, name: Nicolas Heess, name: Martin Riedmiller

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.