Summary of Offline Actor-critic Reinforcement Learning Scales to Large Models, by Jost Tobias Springenberg et al.
Offline Actor-Critic Reinforcement Learning Scales to Large Models
by 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
First submitted to arxiv on: 8 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Robotics (cs.RO)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper demonstrates the scalability of offline actor-critic reinforcement learning (RL) to large models like transformers. The research shows that offline RL algorithms can outperform strong supervised behavioral cloning baselines on a large dataset containing expert and sub-optimal behavior across 132 continuous control tasks. A key contribution is the introduction of a Perceiver-based actor-critic model, which highlights the importance of self- and cross-attention modules in making offline RL work. The findings indicate that simple offline actor-critic algorithms are a natural choice for moving away from behavioral cloning and that offline RL enables learning multi-task policies that can master many domains simultaneously, including real robotics tasks, using sub-optimal demonstrations or self-generated data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper shows that big models like transformers can learn really well without needing to be trained on lots of new data. It also helps us understand how we can use old data in a way that makes sense for learning new things. The researchers looked at many different tasks and found that simple algorithms worked best, especially when they were combined with special attention modules. This is important because it means we might not need to keep training our models on new data all the time. |
Keywords
* Artificial intelligence * Attention * Cross attention * Multi task * Reinforcement learning * Supervised
