オフラインActor-Critic強化学習の大規模モデルへのスケーリング
Offline Actor-Critic Reinforcement Learning Scales to Large Models
オフラインActor-Critic強化学習がTransformerなどの大規模モデルにスケールし、132の連続制御タスクで行動クローニングを上回ることを示した研究。
著者: 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
分類: cs.LG, cs.AI, cs.RO
原文アブストラクト
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.
