Overview

The recent rapid advancement of machine learning has been driven by increasingly powerful models with the growing availability of training data and computational resources. However, real-time decision-making tasks with limited time and sparse learning signals remain challenging. One way of improving the learning speed and performance of these agents is to leverage human guidance. In this work, we introduce GUIDE, a framework for real-time human-guided reinforcement learning by enabling continuous human feedback and grounding such feedback into dense rewards to accelerate policy learning. Additionally, our method features a simulated feedback module that learns and replicates human feedback patterns in an online fashion, effectively reducing the need for human input while allowing continual training. We demonstrate the performance of our framework on challenging tasks with sparse rewards and visual observations. Our human study involving 50 subjects offers strong quantitative and qualitative evidence of the effectiveness of our approach. With only 10 minutes of human feedback, our algorithm achieves up to 30% increase in success rate compared to its RL baseline.

Video (Click to YouTube)

Paper

Check out our paper linked here.

Codebase

Check out our codebase at https://github.com/generalroboticslab/CREW/blob/main/crew-algorithms/README.md

Our implementation is built upon our CREW platform. Check out our full documentation including tutorials to use CREW and detailed descriptions of the designs and structures of CREW at https://generalroboticslab.github.io/crew-docs/

Citation

@misc{zhang2024guiderealtimehumanshapedagents,
      title={GUIDE: Real-Time Human-Shaped Agents}, 
      author={Lingyu Zhang and Zhengran Ji and Nicholas R Waytowich and Boyuan Chen},
      year={2024},
      eprint={2410.15181},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2410.15181}, 
}       

Acknowledgment

This work is supported in part by ARL under awards W911NF2320182 and W911NF2220113.

Contact

If you have any questions, please feel free to contact Lingyu Zhang.