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