GUIDE: Real-Time Human-Shaped Agents
This work addresses the problem of slow learning in real-time agents for tasks with sparse rewards, offering a practical solution for domains requiring human-in-the-loop guidance, though it is incremental in leveraging existing human feedback techniques.
The paper tackles the challenge of real-time decision-making with sparse rewards by introducing GUIDE, a framework that uses continuous human feedback to accelerate reinforcement learning, achieving up to a 30% increase in success rate with only 10 minutes of human input compared to baseline methods.
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.