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cloud.google.com·6h ago
AI Research
arXiv CS.AI·Aug 28·5 min read
Residual Reward Models: Leveraging Prior Knowledge for Efficient Preference-based Reinforcement Learning in Robotics
TL;DR
Residual Reward Models (RRM) enhance preference-based reinforcement learning (PbRL) in robotics by combining prior knowledge with learned rewards, significantly improving sample efficiency. Experiments show RRM accelerates policy learning and increases success rates in real-world robotic tasks.
✦ Why It Matters
Implement Residual Reward Models in your robotics projects to enhance learning efficiency and reduce reliance on human feedback.
Key Takeaways
- Explore integrating prior knowledge into your reinforcement learning models.
- Test Residual Reward Models to improve sample efficiency in robotic tasks.
- Utilize engineering heuristics as prior rewards for better performance.
- Reduce the amount of human feedback needed in your learning processes.
How It Works
Residual Reward Models (RRM) operate by splitting the reward signal into two parts: a prior reward, which is established before training, and a learned reward that is adjusted based on human preferences. This approach allows the model to start with a solid foundation of prior knowledge, which can be fine-tuned through additional learning, leading to more stable optimization and improved performance.