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AI Research
arXiv CS.AI·Jun 9·5 min read
An Agency-Transferring Model-Free Policy Enhancement Technique
✦ Why It Matters
Engineers can leverage existing policies to enhance RL training efficiency and outcomes significantly.
Key Takeaways
- Utilize existing baseline policies to improve RL training efficiency.
- Gradually transfer agency from baseline to learning policy during training.
- Ensure the learning policy can operate independently by training's end.
- Achieve higher goal-reaching rates compared to traditional methods.
How It Works
The proposed method arbitrates between a functional baseline policy and a trainable learning policy. Initially, the learning process relies heavily on the baseline, which ensures high goal-reaching rates.
As training progresses, the method gradually increases the influence of the learning policy, allowing it to take over completely by the end of the training phase. This approach not only enhances training efficiency but also leads to a more capable standalone policy.