# Offline Robot Reinforcement Learning with Uncertainty-Guided Human Expert Sampling

**Authors:** Ashish Kumar, Ilya Kuzovkin  
**arXiv:** [2212.08232](https://arxiv.org/abs/2212.08232)  
**Date:** 2022-12-16  
**Summary page:** https://www.summarizepaper.com/en/paper/2212.08232v1/

## Key points

- Batch (offline) reinforcement learning can learn control policies from available offline data, which is useful in robotics where operational data is often collected by suboptimal non-learning-based algorithms.
- However, there is still room for improvement in the sample complexity of existing offline reinforcement learning algorithms.
- A novel approach has been proposed that strategically introduces human demonstration data into the training process using uncertainty estimation to guide policy training towards optimal behavior while reducing overall sample complexity.
- Experiments have shown that this approach is more sample efficient than a naive way of combining expert data with data collected from a sub-optimal agent.
- The proposed approach involves augmenting an existing offline reinforcement learning algorithm called Conservative Q-Learning with uncertainty-guided human expert sampling.
- The method uses uncertainty estimates generated during policy evaluation to identify areas where additional expert demonstrations would be most beneficial for improving performance.
- By incorporating these demonstrations selectively, the algorithm can improve its performance without requiring as much additional training data.
- This research highlights the potential benefits of incorporating human expertise into machine learning algorithms and demonstrates how uncertainty estimation can be used to guide this process effectively.
- These findings could have important implications for the development of more efficient and effective robot control policies in real-world settings.

## Summary

The field of batch (offline) reinforcement learning has made significant strides in recent years, demonstrating the potential to learn control policies from available offline data. This approach is particularly useful in robotics, where operational data is often collected by suboptimal non-learning-based algorithms. Offline reinforcement learning algorithms can learn from such data and produce policies that outperform the behavior agent used to collect it. However, there is still room for improvement in the sample complexity of existing offline reinforcement learning algorithms. To address this issue, a novel approach has been proposed that strategically introduces human demonstration data into the training process using uncertainty estimation. The goal is to guide policy training towards optimal behavior while reducing overall sample complexity. Experiments conducted on data collected from MuJoCo and OffWorld Gym learning environments have shown that this approach is more sample efficient than a naive way of combining expert data with data collected from a sub-optimal agent. The proposed approach involves augmenting an existing offline reinforcement learning algorithm called Conservative Q-Learning with uncertainty-guided human expert sampling. The method uses uncertainty estimates generated during policy evaluation to identify areas where additional expert demonstrations would be most beneficial for improving performance. By incorporating these demonstrations selectively, the algorithm can improve its performance without requiring as much additional training data. Overall, this research highlights the potential benefits of incorporating human expertise into machine learning algorithms and demonstrates how uncertainty estimation can be used to guide this process effectively. These findings could have important implications for the development of more efficient and effective robot control policies in real-world settings.